def main():
print('Reading subjects')
ctrl_files = read_gord_data(data_dir=CTRL_DIR, num_sub=NUM_CTRL)
t0 = time.time()
print('performing stats based on random pairwise distances')
pval_fdr, pval = compare_sub2ctrl(bfp_path=BFPPATH,
sub_file=SUB_DATA,
ctrl_files=ctrl_files,
num_pairs=2000,
nperm=1000,
len_time=LEN_TIME,
num_proc=4,
fdr_test=True)
t1 = time.time()
print(t1 - t0)
sp.savez('pval_out200.npz', pval=pval, pval_fdr=pval_fdr)
vis_grayord_sigpval(bfp_path=BFPPATH,
pval=pval,
surf_name='subdiff',
out_dir='/big_disk/ajoshi/coding_ground/bfp/src/stats',
smooth_iter=1000)
print('Results saved')
def main():
print('Reading subjects')
_, reg_var, sub_files = read_oasis3_data(
csv_fname=CSV_FILE,
data_dir=DATA_DIR,
reg_var_name='UDSB9', #'Verbal IQ', # #
num_sub=NUM_SUB,
len_time=LEN_TIME,
data_field='SCT_GO')
# Shuffle reg_var and subjects for testing
#reg_var = sp.random.permutation(reg_var)
#ran_perm = sp.random.permutation(len(reg_var))
#reg_var = reg_var
#sub_files = [sub_files[i] for i in range(len(reg_var))]
sub_files = sub_files #[50:100]
reg_var = reg_var #[50:100]
t0 = time.time()
print('performing stats based on random pairwise distances')
corr_pval_max, corr_pval_fdr = randpairs_regression(
bfp_path=BFPPATH,
sub_files=sub_files,
reg_var=reg_var,
num_pairs=20000, # 19900,
nperm=2000,
len_time=LEN_TIME,
num_proc=6,
pearson_fdr_test=False,
data_field='SCT_GO')
t1 = time.time()
print(t1 - t0)
sp.savez('pval_num_pairs20000_nsub350_nperm2000_SCT.npz',
corr_pval_max=corr_pval_max,
corr_pval_fdr=corr_pval_fdr)
# corr_pval_max=a['corr_pval_max']
# corr_pval_fdr=a['corr_pval_fdr']
vis_grayord_sigpval(corr_pval_max,
surf_name='rand_dist_corr_perm_pairs20000_max_SCT',
out_dir='.',
smooth_iter=1000,
bfp_path=BFPPATH,
fsl_path=FSL_PATH,
sig_alpha=0.05)
vis_grayord_sigpval(corr_pval_fdr,
surf_name='rand_dist_corr_perm_pairs20000_fdr_SCT',
out_dir='.',
smooth_iter=1000,
bfp_path=BFPPATH,
fsl_path=FSL_PATH,
sig_alpha=0.05)
print('Results saved')
def main():
print('Reading subjects')
_, reg_var, sub_files = read_fcon1000_data(
csv_fname=CSV_FILE,
data_dir=DATA_DIR,
reg_var_name='ADHD Index', # 'Verbal IQ', # #
num_sub=NUM_SUB)
# Shuffle reg_var and subjects for testing
reg_var = np.random.permutation(reg_var)
#ran_perm = sp.random.permutation(len(reg_var))
#reg_var = reg_var
#sub_files = [sub_files[i] for i in range(len(reg_var))]
#sub_files=sub_files[30:30+15]
#reg_var = reg_var[30:30+15]
t0 = time.time()
print('performing stats based on kernel regression')
pval_kr_ftest, pval_kr_ftest_fdr = kernel_regression_ftest(
bfp_path=BFPPATH,
sub_files=sub_files,
reg_var=reg_var,
nperm=2000,
len_time=LEN_TIME,
num_proc=4,
fdr_test=False,
simulation=False)
t1 = time.time()
print(t1 - t0)
np.savez('pval_KR_ftest.npz',
pval=pval_kr_ftest,
pval_fdr=pval_kr_ftest_fdr)
# corr_pval_max=a['corr_pval_max']
# corr_pval_fdr=a['corr_pval_fdr']
#a=np.load('pval_KR_ftest5null.npz')
#pval_kr_ftest = a['pval']
vis_grayord_sigpval(pval_kr_ftest,
surf_name='KR_pval_ftest',
out_dir='.',
smooth_iter=1000,
bfp_path=BFPPATH,
fsl_path=FSL_PATH,
sig_alpha=0.5)
vis_grayord_sigpval(pval_kr_ftest_fdr,
surf_name='KR_pval_ftest_fdr',
out_dir='.',
smooth_iter=1000,
bfp_path=BFPPATH,
fsl_path=FSL_PATH,
sig_alpha=0.05)
print('Results saved')
bfp_path=cf.bfp_path,
sub_files=subTest_fname,
reg_var=subTest_varmain2,
num_pairs=2000, # 19900,
nperm=2000,
len_time=int(cf.lentime),
num_proc=1,
pearson_fdr_test=True)
#%%
spio.savemat(os.path.join(cf.out_dir + '/' + cf.outname +
'_corr_pval_pearson.mat'),
{'corr_pval_fdr':corr_pval_fdr,
'corr_pval':corr_pval})
#%% Visualization of the results
vis_grayord_sigpval(corr_pval_fdr,
float(cf.sig_alpha),
surf_name=cf.outname + '_fdr',
out_dir=cf.out_dir,
smooth_iter=int(cf.smooth_iter),
bfp_path=cf.bfp_path,
fsl_path=cf.fsl_path)
vis_grayord_sigpval(corr_pval,
float(cf.sig_alpha),
surf_name=cf.outname + 'uncorr',
out_dir=cf.out_dir,
smooth_iter=int(cf.smooth_iter),
bfp_path=cf.bfp_path,
fsl_path=cf.fsl_path)
write_text_timestamp(log_fname, 'BFP regression analysis complete')
bfp_path=cf.bfp_path,
sub_files=subTest_fname,
reg_var=subTest_varmain,
num_pairs=int(cf.pw_pairs), # 19900,
nperm=int(cf.pw_perm),
len_time=int(cf.lentime),
num_proc=1,
pearson_fdr_test=bool(cf.pw_fdr))
# saves out results
spio.savemat(os.path.join(cf.out_dir + '/' + cf.outname + '_corr_pval_max.mat'), {'corr_pval_max': corr_pval_max})
spio.savemat(os.path.join(cf.out_dir + '/' + cf.outname + '_corr_pval_fdr.mat'), {'corr_pval_fdr': corr_pval_fdr})
#Visualization of the results
vis_grayord_sigpval(corr_pval_max, float(cf.sig_alpha),
surf_name=cf.outname + '_max',
out_dir=cf.out_dir,
smooth_iter=int(cf.smooth_iter),
bfp_path=cf.bfp_path,
fsl_path=cf.fsl_path,
save_png=bool(cf.save_figures))
vis_grayord_sigpval(corr_pval_fdr, float(cf.sig_alpha),
surf_name=cf.outname + 'fdr',
out_dir=cf.out_dir,
smooth_iter=int(cf.smooth_iter),
bfp_path=cf.bfp_path,
fsl_path=cf.fsl_path,
save_png=bool(cf.save_figures))
write_text_timestamp(log_fname, 'BFP regression analysis complete! '+
'\n pvalue labeled surfaces have colorbar limits 0 to ' + str(cf.sig_alpha) + '; colorbar class is jet reverse.'+
'\n significance indicates that brain connectivty is associated with '+ cf.colvar_main +