def main():
studydir = '/ImagePTE1/ajoshi/fitbir/preproc/maryland_rao_v1'
epi_txt = '/ImagePTE1/ajoshi/fitbir/preproc/maryland_rao_v1_epilepsy_imgs.txt'
nonepi_txt = '/ImagePTE1/ajoshi/fitbir/preproc/maryland_rao_v1_nonepilepsy_imgs_37.txt'
with open(epi_txt) as f:
epiIds = f.readlines()
with open(nonepi_txt) as f:
nonepiIds = f.readlines()
epiIds = list(map(lambda x: x.strip(), epiIds))
nonepiIds = list(map(lambda x: x.strip(), nonepiIds))
epi_files = list()
nonepi_files = list()
for sub in epiIds:
fname = os.path.join(studydir, sub, 'BFP', sub, 'func',
sub + '_rest_bold.32k.GOrd.mat')
if os.path.isfile(fname):
epi_files.append(fname)
for sub in nonepiIds:
fname = os.path.join(studydir, sub, 'BFP', sub, 'func',
sub + '_rest_bold.32k.GOrd.mat')
if os.path.isfile(fname):
nonepi_files.append(fname)
epi_data = load_bfp_data(epi_files, 171)
nonepi_data = load_bfp_data(nonepi_files, 171)
t = time.time()
X2, Os, Costdif, TotalError = groupBrainSync(nonepi_data)
elapsed = time.time() - t
np.savez('grp_atlas2.npz', X2=X2, Os=Os)
atlas_data, _, _ = normalizeData(np.mean(X2, axis=1))
np.savez('grp_atlas.npz', atlas_data=atlas_data)
# Do Pointwise stats
# pointwise_stats(epi_data, nonepi_data)
vis_grayord_sigcorr(pval, rval, cf.outname, cf.out_dir,
int(cf.smooth_iter), cf.save_surfaces, cf.save_figures,
'True')
print('done')
pbar = tqdm(total=len(subj))
for i in range(len(subj)):
print(str(n))
n = n + 1
fname = os.path.join(dirname, subj[i], 'func', subj[i] + ext)
if os.path.isfile(fname):
df = spio.loadmat(fname)
data = df['dtseries'].T
if int(data.shape[0]) == LenTime:
sub_ID.append(subj[i])
sub_fname.append(fname)
pbar.update(1)
np.savetxt(out_dir + "/subjects.csv", sub_ID, delimiter=",", fmt='%s')
write_text_timestamp(
flog, 'data for ' + str(len(sub_ID)) +
' subjects will be used for atlas creation')
sub_data = load_bfp_data(sub_fname, LenTime)
#%% run group sync
groupatlas_data, _, _, _ = groupBrainSync(sub_data)
spio.savemat(os.path.join(out_dir + '/group_atlas.mat'),
{'groupatlas_data': groupatlas_data})
write_text_timestamp(flog, 'completed group sync')
#%% find representative subject
subRef_data, q = IDrefsub_BrainSync(sub_data)
write_text_timestamp(flog, 'representative subject is ' + str(sub_ID[q]))
#%% sync group atlas to representative subject
atlas_data, _ = brainSync(subRef_data, groupatlas_data)
spio.savemat(os.path.join(out_dir + '/atlas.mat'), {'atlas_data': atlas_data})
write_text_timestamp(
flog,
'group atlas synced to representative subject. group-MDS_atlas done.')
def bfp_lincorr(config_file):
#%%
config_file = '/home/sychoi/Documents/MATLAB/bfp/src/stats/sample_config_stats.ini'
#%%#%%
### Import the required librariesimport configparser
import sys
import os
import scipy.io as spio
import scipy as sp
import numpy as np
import configparser
### get BFP directory from config file
config = configparser.ConfigParser()
config.read(config_file)
section = config.sections()
bfp_path = config.get('inputs', 'bfp_path')
sys.path.append(os.path.join(bfp_path, 'src/stats/'))
from dev_config import readConfig
cf = readConfig(config_file)
### Import BrainSync libraries
sys.path.append(os.path.join(str(cf.bfp_path), 'src/BrainSync/'))
from brainsync import IDrefsub_BrainSync, groupBrainSync, generate_avgAtlas
sys.path.append(cf.bfp_path + "/src/stats/")
from stats_utils import dist2atlas, load_bfp_data, read_demoCSV, sync2atlas, multiLinReg_corr
from grayord_utils import vis_grayord_sigcorr
#%%
os.chdir(bfp_path)
print(cf.out_dir + ": writing output directory")
if not os.path.isdir(cf.out_dir):
os.makedirs(cf.out_dir)
# read demographic csv file
sub_ID, sub_fname, subAtlas_idx, reg_var, reg_cvar1, reg_cvar2 = read_demoCSV(
cf.csv_fname, cf.data_dir, cf.file_ext, cf.colsubj, cf.colvar_exclude,
cf.colvar_atlas, cf.colvar_main, cf.colvar_reg1, cf.colvar_reg2)
#%% makes file list for subjects
print('Identifying subjects for atlas creation and hypothesis testing...')
subTest_fname = []
subTest_IDs = []
subAtlas_fname = []
subAtlas_IDs = []
for ind in range(len(sub_ID)):
sub = sub_ID[ind]
fname = sub_fname[ind]
if int(subAtlas_idx[ind]) != 1:
subTest_fname.append(fname)
subTest_IDs.append(sub)
else:
subAtlas_fname.append(fname)
subAtlas_IDs.append(sub)
len(subTest_IDs)
count1 = 0
subTest_varmain = sp.zeros(len(subTest_IDs))
subTest_varc1 = sp.zeros(len(subTest_IDs))
subTest_varc2 = sp.zeros(len(subTest_IDs))
for ind in range(len(sub_ID)):
varmain = reg_var[ind]
varc1 = reg_cvar1[ind]
varc2 = reg_cvar2[ind]
if int(subAtlas_idx[ind]) != 1:
subTest_varmain[count1] = varmain
subTest_varc1[count1] = varc1
subTest_varc2[count1] = varc2
count1 += 1
np.savetxt(cf.out_dir + "/subjects_testing.csv",
subTest_IDs,
delimiter=",",
fmt='%s')
np.savetxt(cf.out_dir + "/subjects_atlas.csv",
subAtlas_IDs,
delimiter=",",
fmt='%s')
print(
str(len(subAtlas_IDs)) + ' subjects will be used for atlas creation.')
print(
str(len(subTest_IDs)) +
' subjects will be used for hypothesis testing.')
#%%
# reads reference data and creates atlas by BrainSync algorithm
subAtlas_data = load_bfp_data(subAtlas_fname, cf.LenTime)
if cf.useGroupSync == True:
print(
'User Option: Group BrainSync algorithm will be used for atlas creation'
)
atlas_data, _, _, _ = groupBrainSync(subAtlas_data)
else:
print(
'User Option: representative subject will be used for atlas creation'
)
subRef_data, subRef_num = IDrefsub_BrainSync(subAtlas_data)
print('Subject number ' + str(subRef_num) +
' will be used for atlas creation')
atlas_data = generate_avgAtlas(subRef_data, subAtlas_data)
spio.savemat(os.path.join(cf.out_dir + '/atlas.mat'),
{'atlas_data': atlas_data})
del subAtlas_data
#%% sync and calculates geodesic distances
subTest_data = load_bfp_data(subTest_fname, cf.LenTime)
subTest_syndata = sync2atlas(atlas_data, subTest_data)
subTest_diff = dist2atlas(atlas_data, subTest_syndata)
spio.savemat(os.path.join(cf.out_dir + '/dist2atlas.mat'),
{'subTest_diff': subTest_diff})
del subTest_data, subTest_syndata
#%% computes correlation after controlling for two covariates
rval, pval, pval_fdr = multiLinReg_corr(subTest_diff, subTest_varmain,
subTest_varc1, subTest_varc2)
#%%
vis_grayord_sigcorr(pval, rval, cf.outname, cf.out_dir, cf.smooth_iter)
vis_grayord_sigcorr(pval_fdr, rval, cf.outname + '_fdr', cf.out_dir,
cf.smooth_iter)
visdata_grayord(pval, surf_name, out_dir, cf.smooth_iter, colorbar_lim,
colormap)