def get_curvature_from_test(hemi='lh'):
"""
这里就是把45个retest被试在test session里的曲率拿过来了
"""
import nibabel as nib
from magicbox.io.io import save2nifti
# inputs
src_file = pjoin(proj_dir, f'analysis/s2/{hemi}/curvature.nii.gz')
subj_test_file = pjoin(proj_dir, 'analysis/s2/subject_id')
subj_retest_file = pjoin(work_dir, 'subject_id')
# outputs
out_file = pjoin(work_dir, f'curvature_{hemi}_ses-test.nii.gz')
# prepare
curv_maps = nib.load(src_file).get_fdata()
subj_ids_test = open(subj_test_file).read().splitlines()
subj_ids_retest = open(subj_retest_file).read().splitlines()
retest_indices = [subj_ids_test.index(i) for i in subj_ids_retest]
# calculate
curv_retest = curv_maps[..., retest_indices]
# save
save2nifti(out_file, curv_retest)
def calc_prob_map(hemi='lh'):
import numpy as np
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import roi2label
from magicbox.io.io import save2nifti
# inputs
n_roi = len(roi2label)
print(n_roi)
roi_file = pjoin(work_dir, f'rois_v3_{hemi}.nii.gz')
# outputs
out_file = pjoin(work_dir, f'prob_maps_v3_{hemi}.nii.gz')
# prepare
rois = nib.load(roi_file).get_fdata().squeeze().T
n_vtx = rois.shape[1]
# calculate
prob_maps = np.ones((n_roi, n_vtx)) * np.nan
for idx, roi in enumerate(roi2label.keys()):
label = roi2label[roi]
print(roi)
# get indices of subjects which contain the roi
indices = rois == label
subj_indices = np.any(indices, 1)
# calculate roi probability map among valid subjects
prob_map = np.mean(indices[subj_indices], 0)
prob_maps[idx] = prob_map
# save out
save2nifti(out_file, prob_maps.T[:, None, None, :])
def roi_stats(gid=1, hemi='lh'):
import numpy as np
import nibabel as nib
import pickle as pkl
from cxy_hcp_ffa.lib.predefine import roi2label
from magicbox.io.io import save2nifti
# inputs
rois = ('IOG-face', 'pFus-face', 'mFus-face')
gid_file = pjoin(work_dir, f'group_id_{hemi}.npy')
roi_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
f'rois_v3_{hemi}.nii.gz')
# outputs
rois_info_file = pjoin(work_dir, f'rois_info_{gid}_{hemi}.pkl')
prob_maps_file = pjoin(work_dir, f'prob_maps_{gid}_{hemi}.nii.gz')
# load
gid_idx_vec = np.load(gid_file) == gid
roi_maps = nib.load(roi_file).get_data().squeeze().T[gid_idx_vec]
# prepare
rois_info = dict()
prob_maps = np.zeros((roi_maps.shape[1], 1, 1, len(rois)),
dtype=np.float64)
# calculate
for roi_idx, roi in enumerate(rois):
label = roi2label[roi]
rois_info[roi] = dict()
# get indices of subjects which contain the roi
indices = roi_maps == label
subj_indices = np.any(indices, 1)
# calculate the number of the valid subjects
n_subject = np.sum(subj_indices)
rois_info[roi]['n_subject'] = n_subject
# calculate roi sizes for each valid subject
sizes = np.sum(indices[subj_indices], 1)
rois_info[roi]['sizes'] = sizes
# calculate roi probability map among valid subjects
prob_map = np.mean(indices[subj_indices], 0)
prob_maps[:, 0, 0, roi_idx] = prob_map
# save
pkl.dump(rois_info, open(rois_info_file, 'wb'))
save2nifti(prob_maps_file, prob_maps)
def get_mpm(gh_id=11, hemi='lh'):
"""maximal probability map"""
import numpy as np
import nibabel as nib
from commontool.io.io import save2nifti
thr = 0.25
prob_map_file = pjoin(split_dir, f'prob_maps_GH{gh_id}_{hemi}.nii.gz')
out_file = pjoin(split_dir, f'MPM_GH{gh_id}_{hemi}.nii.gz')
prob_maps = nib.load(prob_map_file).get_data()
supra_thr_idx_arr = prob_maps > thr
prob_maps[~supra_thr_idx_arr] = 0
mpm = np.argmax(prob_maps, 3)
mpm[np.any(prob_maps, 3)] += 1
# save
save2nifti(out_file, mpm)
def get_mpm(hemi='lh'):
"""maximal probability map"""
import numpy as np
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import roi2label
from magicbox.io.io import save2nifti
# inputs
thr = 0.25
map_indices = [1, 2]
idx2roi = {0: 'IOG-face', 1: 'pFus-face', 2: 'mFus-face'}
prob_file = pjoin(work_dir, f'prob_maps_v3_{hemi}.nii.gz')
# outputs
out_file = pjoin(work_dir, f'MPM_v3_{hemi}_{thr}_FFA.nii.gz')
# prepare
prob_maps = nib.load(prob_file).get_fdata()[..., map_indices]
mpm_map = np.zeros(prob_maps.shape[:3])
# calculate
supra_thr_idx_arr = prob_maps > thr
valid_idx_arr = np.any(supra_thr_idx_arr, 3)
valid_arr = prob_maps[valid_idx_arr, :]
mpm_tmp = np.argmax(valid_arr, -1)
for i, idx in enumerate(map_indices):
roi = idx2roi[idx]
idx_arr = np.zeros_like(mpm_map, dtype=np.bool8)
idx_arr[valid_idx_arr] = mpm_tmp == i
mpm_map[idx_arr] = roi2label[roi]
# verification
valid_supra_thr_idx_arr = supra_thr_idx_arr[valid_idx_arr, :]
valid_count_vec = np.sum(valid_supra_thr_idx_arr, -1)
valid_count_vec_uniq = np.zeros_like(valid_count_vec)
for i in range(len(valid_count_vec)):
valid_supra_thr_idx_vec = valid_supra_thr_idx_arr[i]
valid_count_vec_uniq[i] = \
len(set(valid_arr[i, valid_supra_thr_idx_vec]))
assert np.all(valid_count_vec == valid_count_vec_uniq)
# save
save2nifti(out_file, mpm_map)
def calc_mean_meas_map(meas='thickness'):
"""
得到1080个被试的平均map
"""
import numpy as np
from magicbox.io.io import CiftiReader, save2nifti
from cxy_hcp_ffa.lib.predefine import hemi2stru
# inputs
hemis = ('lh', 'rh')
subj_file = pjoin(proj_dir, 'analysis/s2/subject_id')
meas_id_file = pjoin(proj_dir, 'data/HCP/subject_id_1096')
meas2file = {
'thickness':
'/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.thickness_MSMAll.32k_fs_LR.dscalar.nii',
'myelin':
'/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.MyelinMap_BC_MSMAll.32k_fs_LR.dscalar.nii',
'va':
'/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.midthickness_MSMAll_va.32k_fs_LR.dscalar.nii'
}
# outputs
out_file = pjoin(work_dir, '{meas}_mean-1080_{hemi}.nii.gz')
# prepare
subj_ids = open(subj_file).read().splitlines()
meas_reader = CiftiReader(meas2file[meas])
meas_ids = open(meas_id_file).read().splitlines()
meas_indices = [meas_ids.index(i) for i in subj_ids]
# calculate
for hemi in hemis:
meas_maps = meas_reader.get_data(hemi2stru[hemi], True)[meas_indices]
meas_mean = np.mean(meas_maps, 0)
meas_mean = np.expand_dims(meas_mean, (1, 2))
save2nifti(out_file.format(meas=meas, hemi=hemi), meas_mean)
def get_curvature(hemi='lh'):
"""
把45个retest被试在retest session的curvature合并成左右脑两个nifti文件，
主要是为了我那个程序在标定个体ROI的时候读取和显示曲率。
之前服务器上没有retest的结构数据，我想当然地认为同一个被试的沟回曲率在
两次测量应该是一模一样的，所以在标定v1和v2版ROI的时候参照的是test session的曲率；
这次我下了retest的结构数据，决定用retest session的曲率校对一下retest个体ROI。
"""
import numpy as np
from magicbox.io.io import CiftiReader, save2nifti
from cxy_hcp_ffa.lib.predefine import hemi2stru
# inputs
hemis = ('lh', 'rh')
fpath = '/nfs/m1/hcp/retest/{0}/MNINonLinear/fsaverage_LR32k/'\
'{0}.curvature_MSMAll.32k_fs_LR.dscalar.nii'
subj_id_file = pjoin(work_dir, 'subject_id')
# outputs
out_file = pjoin(work_dir, 'curvature_{hemi}.nii.gz')
# prepare
subj_ids = open(subj_id_file).read().splitlines()
n_subj = len(subj_ids)
hemi2data = {}
for hemi in hemis:
hemi2data[hemi] = np.zeros((32492, 1, 1, n_subj), np.float64)
# calculate
for subj_idx, subj_id in enumerate(subj_ids):
reader = CiftiReader(fpath.format(subj_id))
for hemi in hemis:
data_tmp = reader.get_data(hemi2stru[hemi], True)[0]
hemi2data[hemi][:, 0, 0, subj_idx] = data_tmp
print(f'Finished: {subj_idx+1}/{n_subj}')
# save
for hemi in hemis:
save2nifti(out_file.format(hemi=hemi), hemi2data[hemi])
def calc_prob_map():
import numpy as np
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import roi2label
from magicbox.io.io import save2nifti
hemis = ('lh', 'rh')
rois = ('IOG-face', 'pFus-face', 'mFus-face')
n_roi = len(rois)
gh_ids = (1, 2, 11, 12, 21, 22)
gh_id_file = pjoin(split_dir, 'half_id_{}.npy')
roi_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
'rois_v3_{}.nii.gz')
out_file = pjoin(split_dir, 'prob_maps_GH{}_{}.nii.gz')
for hemi in hemis:
gh_id_vec = np.load(gh_id_file.format(hemi))
roi_maps = nib.load(roi_file.format(hemi)).get_data().squeeze().T
for gh_id in gh_ids:
gh_id_idx_vec = gh_id_vec == gh_id
roi_maps_tmp = roi_maps[gh_id_idx_vec]
prob_maps = np.zeros((n_roi, roi_maps.shape[1]))
for idx, roi in enumerate(rois):
label = roi2label[roi]
# get indices of subjects which contain the roi
indices = roi_maps_tmp == label
subj_indices = np.any(indices, 1)
# calculate roi probability map among valid subjects
prob_map = np.mean(indices[subj_indices], 0)
prob_maps[idx] = prob_map
# save out
save2nifti(out_file.format(gh_id, hemi), prob_maps.T[:, None,
None, :])