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