def filter_twinsID_rfMRI():
"""
Check if a subject have all 4 rfMRI runs.
If not, filter twins which don't have all 4 rfMRI runs.
"""
import numpy as np
import pandas as pd
twins_file = pjoin(work_dir, 'twins_id.csv')
subjs_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
'rfMRI/rfMRI_REST_id')
trg_file = pjoin(work_dir, 'twins_id_rfMRI.csv')
twins_df = pd.read_csv(twins_file)
subjs_twin = set(np.concatenate([twins_df['twin1'], twins_df['twin2']]))
subjs_id = set([int(_) for _ in open(subjs_file).read().splitlines()])
flag = subjs_twin.issubset(subjs_id)
if flag:
print('All twins have all 4 rfMRI runs')
else:
print("Filter twins which don't have all 4 rfMRI runs.")
h2.filter_twins_id(data=twins_df,
limit_set=subjs_id,
trg_file=trg_file)
h2.count_twins_id(trg_file)
def filter_twinsID_G1G2():
"""
Check if the subject is in G1 or G2.
If not, filter twins who are not in G1 or G2.
"""
import numpy as np
import pandas as pd
hemis = ('lh', 'rh')
twins_file = pjoin(work_dir, 'twins_id.csv')
gid_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
'grouping/group_id_{hemi}.npy')
subjs_file = pjoin(proj_dir, 'analysis/s2/subject_id')
trg_file = pjoin(work_dir, 'twins_id_G1G2_{hemi}.csv')
twins_df = pd.read_csv(twins_file)
subjs_twin = set(np.concatenate([twins_df['twin1'], twins_df['twin2']]))
subjs_1080 = np.array(
[int(_) for _ in open(subjs_file).read().splitlines()])
for hemi in hemis:
gid_vec = np.load(gid_file.format(hemi=hemi))
gid_idx_vec = np.logical_or(gid_vec == 1, gid_vec == 2)
subjs_id = subjs_1080[gid_idx_vec]
flag = subjs_twin.issubset(subjs_id)
if flag:
print('All twins are in G1 and G2')
else:
print("Filter twins who are not in G1 or G2.")
trg_file_tmp = trg_file.format(hemi=hemi)
h2.filter_twins_id(data=twins_df,
limit_set=subjs_id,
trg_file=trg_file_tmp)
h2.count_twins_id(trg_file_tmp)
def get_twinsID():
"""
Get twins ID according to 'ZygosityGT' and pair the twins according to
'Family_ID' from HCP restricted information.
"""
src_file = '/nfs/m1/hcp/S1200_behavior_restricted.csv'
trg_file = pjoin(work_dir, 'twins_id.csv')
h2.get_twins_id(src_file, trg_file)
h2.count_twins_id(trg_file)
def filter_twinsID_1080():
"""
Check if it's a subset of 1080 subjects.
If not, filter twins which are not in 1080 subjects.
"""
import numpy as np
import pandas as pd
twins_file = pjoin(work_dir, 'twins_id.csv')
subjs_file = pjoin(proj_dir, 'analysis/s2/subject_id')
trg_file = pjoin(work_dir, 'twins_id_1080.csv')
twins_df = pd.read_csv(twins_file)
subjs_twin = set(np.concatenate([twins_df['twin1'], twins_df['twin2']]))
subjs_id = set([int(_) for _ in open(subjs_file).read().splitlines()])
flag = subjs_twin.issubset(subjs_id)
if flag:
print('All twins is a subset of 1080 subjects.')
else:
print('Filter twins which are not in 1080 subjects.')
h2.filter_twins_id(data=twins_df,
limit_set=subjs_id,
trg_file=trg_file)
h2.count_twins_id(trg_file)