if not is_done(chkf, step): group_filter(outdir, mdir, gname, outf_group) checklist_add(chkf, step) step = measureperslice_str( ) + ' ' + measure_fname + ' ' + stats_str( ) + ' ' + gname + ' ' + mdir if not is_done(chkf, step): group_stats(mdir, gname) checklist_add(chkf, step) step = postmerging_str( ) + ' ' + measure_fname + ' ' + stats_str( ) + ' ' + gname + ' ' + mdir if not is_done(chkf, step): post.merge_stats_slices(mdir, gname) checklist_add(chkf, step) np.savetxt(mdir + os.path.sep + groupsizes_str(), gsize, fmt='%i,%i') step = postmerging_str() + ' ' + measure_fname + str( classnames) + ' ' + mdir if measure == 'bat': if not is_done(chkf, step): print( 'Calculating Univariate Gaussian Bhattacharyya distances' ) bat.bhattacharyya_distance(mdir, classnames, chkf, foldnumber, expname)
step = au.groupfilter_str() + ' ' + gname + ' ' + statsdir if not au.is_done(chkf, step): au.group_filter (outdir, statsdir, gname, outf_group, usemask) au.checklist_add(chkf, step) grp_step_params = ' ' + au.stats_str() + ' ' + gname + ' ' + statsdir step = au.measureperslice_str() + grp_step_params if not au.is_done(chkf, step): post.group_stats (statsdir, gname, gsize[c,1], statsdir) au.checklist_add(chkf, step) statfnames = {} step = au.postmerging_str() + grp_step_params if not au.is_done(chkf, step): statfnames[gname] = post.merge_stats_slices (statsdir, gname) au.checklist_add(chkf, step) sampsizef = mdir + os.path.sep + au.groupsizes_str() np.savetxt(sampsizef, gsize, fmt='%i,%i') #decide which group distance function to use if measure == 'bat': distance_func = bat.measure_bhattacharyya_distance elif measure == 'ttest': distance_func = ttst.measure_ttest #now we deal with the indexed excluded subject step = au.postmerging_str() + ' ' + str(classnames) + step_params exsubf = '' exclas = ''
if not au.is_done(chkf, step): au.group_filter(outdir, statsdir, gname, outf_group, usemask) au.checklist_add(chkf, step) grp_step_params = ' ' + au.stats_str( ) + ' ' + gname + ' ' + statsdir step = au.measureperslice_str() + grp_step_params if not au.is_done(chkf, step): post.group_stats(statsdir, gname, gsize[c, 1], statsdir) au.checklist_add(chkf, step) statfnames = {} step = au.postmerging_str() + grp_step_params if not au.is_done(chkf, step): statfnames[gname] = post.merge_stats_slices( statsdir, gname) au.checklist_add(chkf, step) sampsizef = mdir + os.path.sep + au.groupsizes_str() np.savetxt(sampsizef, gsize, fmt='%i,%i') #decide which group distance function to use if measure == 'bat': distance_func = bat.measure_bhattacharyya_distance elif measure == 'ttest': distance_func = ttst.measure_ttest #now we deal with the indexed excluded subject step = au.postmerging_str() + ' ' + str(classnames) + step_params exsubf = '' exclas = ''
np.savetxt(outf_labels, glabels, fmt='%i') np.savetxt(outf_group , gselect, fmt='%i') step = groupfilter_str() + ' ' + measure_fname + ' ' + stats_str() + ' ' + gname + ' ' + mdir if not is_done(chkf, step): group_filter (outdir, mdir, gname, outf_group) checklist_add(chkf, step) step = measureperslice_str() + ' ' + measure_fname + ' ' + stats_str() + ' ' + gname + ' ' + mdir if not is_done(chkf, step): group_stats (mdir, gname) checklist_add(chkf, step) step = postmerging_str() + ' ' + measure_fname + ' ' + stats_str() + ' ' + gname + ' ' + mdir if not is_done(chkf, step): post.merge_stats_slices (mdir, gname) checklist_add(chkf, step) np.savetxt(mdir + os.path.sep + groupsizes_str(), gsize, fmt='%i,%i') step = postmerging_str() + ' ' + measure_fname + str(classnames) + ' ' + mdir if measure == 'bat': if not is_done(chkf, step): print ('Calculating Univariate Gaussian Bhattacharyya distances') bat.bhattacharyya_distance (mdir, classnames, chkf, foldnumber, expname) elif measure == 'ttest': if not is_done(chkf, step): print ('Calculating Student t-test') ttst.student_ttest (mdir, classnames, gsize, chkf, foldnumber, expname) #adding step end indication