np.savetxt(fnc_corr_outfile, fnc_corr, fmt='%1.5f', delimiter='\t')
fnc_corr_z_outfile = os.path.join(datadir, fnc_corr_z_out)
np.savetxt(fnc_corr_z_outfile, fnc_corr_z, fmt='%1.5f', delimiter='\t')
fnc_lag_outfile = os.path.join(datadir, fnc_lag_out)
np.savetxt(fnc_lag_outfile, fnc_lag, fmt='%1.2f', delimiter='\t')
## Run group analysis
#######################
exists, resultsdir = gu.make_dir(datadir,'randomise')
resultsglob = os.path.join(datadir, 'FNCtb_*.csv')
result_files = glob(resultsglob)
for fnc_data_file in result_files:
fnc_data = np.genfromtxt(fnc_data_file, names=None, dtype=float, delimiter=None)
pth, fname, ext = gu.split_filename(fnc_data_file)
fnc_img_fname = os.path.join(resultsdir, fname + '.nii.gz')
fnc_saveimg = gu.save_img(fnc_data, fnc_img_fname)
rand_basename = os.path.join(resultsdir, fname)
p_uncorr_list, p_corr_list = ga.randomise(fnc_saveimg,
rand_basename,
des_file,
con_file)
uncorr_results = ga.get_results(p_uncorr_list)
corr_results = ga.get_results(p_corr_list)
fdr_results = {}
for i in range(len(uncorr_results.keys())):
conname = sorted(uncorr_results.keys())[i]
fdr_corr_arr = ga.multi_correct(uncorr_results[conname])
fdr_results[conname] = gu.square_from_combos(fdr_corr_arr, nnodes)
outfile = os.path.join(resultsdir,
modeldir = '/home/jagust/rsfmri_ica/GIFT/models/Old'
des_file = os.path.join(modeldir, 'Covariate_Old_log_demeaned.mat')
con_file = os.path.join(modeldir, 'Covariate_Old_log_demeaned.con')
resultsglob = '*mancovan_preproc.csv'
##############################################################
## Run group analysis with randomise
####################################
exists, resultsdir = gu.make_dir(basedir,'randomise')
result_files = glob(os.path.join(datadir,resultsglob))
for data_file in result_files:
data = np.genfromtxt(data_file, names=None, dtype=float, delimiter=',')
pth, fname, ext = gu.split_filename(data_file)
img_fname = os.path.join(resultsdir, fname + '.nii.gz')
saveimg = gu.save_img(data, img_fname)
rand_basename = os.path.join(resultsdir, fname)
p_uncorr_list, p_corr_list = ga.randomise(saveimg,
rand_basename,
des_file,
con_file)
uncorr_results = ga.get_results(p_uncorr_list)
corr_results = ga.get_results(p_corr_list)
fdr_results = {}
for i in range(len(uncorr_results.keys())):
conname = sorted(uncorr_results.keys())[i]
fdr_corr_arr = ga.multi_correct(uncorr_results[conname])
fdr_results[conname] = gu.square_from_combos(fdr_corr_arr, nnodes)
outfile = os.path.join(resultsdir,
# Get correlation and lag matrices
mfnc_zcorr = go.get_mfnc_stats(infile)
# Save out text files of correlation and lags
mfnc_zcorr_outfile = os.path.join(datadir, mfnc_zcorr_out)
np.savetxt(mfnc_zcorr_outfile, mfnc_zcorr, fmt='%1.5f', delimiter='\t')
## Run group analysis
#######################
exists, resultsdir = gu.make_dir(datadir,'randomise')
resultsglob = os.path.join(datadir, 'mfnc_zcorr.csv')
result_files = glob(resultsglob)
for mfnc_data_file in result_files:
mfnc_data = np.genfromtxt(mfnc_data_file, names=None, dtype=float, delimiter=None)
pth, fname, ext = gu.split_filename(mfnc_data_file)
mfnc_img_fname = os.path.join(resultsdir, fname + '.nii.gz')
mfnc_saveimg = gu.save_img(mfnc_data, mfnc_img_fname)
rand_basename = os.path.join(resultsdir, fname)
p_uncorr_list, p_corr_list = ga.randomise(mfnc_saveimg,
rand_basename,
des_file,
con_file)
uncorr_results = ga.get_results(p_uncorr_list)
corr_results = ga.get_results(p_corr_list)
fdr_results = {}
for i in range(len(uncorr_results.keys())):
conname = sorted(uncorr_results.keys())[i]
fdr_corr_arr = ga.multi_correct(uncorr_results[conname])
fdr_results[conname] = gu.square_from_combos(fdr_corr_arr, nnodes)
outfile = os.path.join(resultsdir,
allsub_array[i] = sub_stat
outname = '_'.join(['dFNC', measure_name, stat_name]) + '.csv'
outfile = os.path.join(datadir, outname)
np.savetxt(outfile, allsub_array, fmt='%1.5f', delimiter='\t')
## Run group analysis with randomise
####################################
exists, resultsdir = gu.make_dir(datadir,'randomise')
resultsglob = os.path.join(datadir, 'dFNC_*.csv')
result_files = glob(resultsglob)
for dfnc_data_file in result_files:
dfnc_data = np.genfromtxt(dfnc_data_file, names=None, dtype=float, delimiter=None)
pth, fname, ext = gu.split_filename(dfnc_data_file)
dfnc_img_fname = os.path.join(resultsdir, fname + '.nii.gz')
dfnc_saveimg = gu.save_img(dfnc_data, dfnc_img_fname)
rand_basename = os.path.join(resultsdir, fname)
p_uncorr_list, p_corr_list = ga.randomise(dfnc_saveimg,
rand_basename,
des_file,
con_file)
uncorr_results = ga.get_results(p_uncorr_list)
corr_results = ga.get_results(p_corr_list)
fdr_results = {}
for i in range(len(uncorr_results.keys())):
conname = sorted(uncorr_results.keys())[i]
fdr_corr_arr = ga.multi_correct(uncorr_results[conname])
fdr_results[conname] = gu.square_from_combos(fdr_corr_arr, nnodes)
outfile = os.path.join(resultsdir,