def volstats (invol, groupname, groupsize, outdir=''):
slicesdir = os.path.dirname(invol)
if not outdir:
outdir = slicesdir
base = os.path.basename(au.remove_ext(invol))
outmeanf = outdir + os.path.sep + base + '_' + au.mean_str()
outvarf = outdir + os.path.sep + base + '_' + au.var_str()
outstdf = outdir + os.path.sep + base + '_' + au.std_str()
outsumsf = outdir + os.path.sep + base + '_' + au.sums_str()
vol = nib.load(invol).get_data()
aff = nib.load(invol).get_affine()
if not os.path.exists(outmeanf):
mean = np.mean(vol, axis=3)
au.save_nibabel(outmeanf, mean, aff)
if not os.path.exists(outstdf):
std = np.std(vol, axis=3)
au.save_nibabel(outstdf, std, aff)
if not os.path.exists(outvarf):
var = np.var(vol, axis=3)
au.save_nibabel(outvarf, var, aff)
if not os.path.exists(outsumsf):
sums = np.sum(vol, axis=3)
au.save_nibabel(outsumsf, sums, aff)
return [outsumsf,outmeanf,outvarf,outstdf]
def volstats(invol, groupname, groupsize, outdir=''):
slicesdir = os.path.dirname(invol)
if not outdir:
outdir = slicesdir
base = os.path.basename(au.remove_ext(invol))
outmeanf = outdir + os.path.sep + base + '_' + au.mean_str()
outvarf = outdir + os.path.sep + base + '_' + au.var_str()
outstdf = outdir + os.path.sep + base + '_' + au.std_str()
outsumsf = outdir + os.path.sep + base + '_' + au.sums_str()
vol = nib.load(invol).get_data()
aff = nib.load(invol).get_affine()
if not os.path.exists(outmeanf):
mean = np.mean(vol, axis=3)
au.save_nibabel(outmeanf, mean, aff)
if not os.path.exists(outstdf):
std = np.std(vol, axis=3)
au.save_nibabel(outstdf, std, aff)
if not os.path.exists(outvarf):
var = np.var(vol, axis=3)
au.save_nibabel(outvarf, var, aff)
if not os.path.exists(outsumsf):
sums = np.sum(vol, axis=3)
au.save_nibabel(outsumsf, sums, aff)
return [outsumsf, outmeanf, outvarf, outstdf]
def get_stats_fnames (groupnames, outdir=''):
if np.ndim(groupnames) == 0:
groupnames = [groupnames]
if outdir:
outdir += outdir + os.path.sep
mnames = [au.sums_str(), au.mean_str(), au.var_str(), au.std_str()]
ngroups = len(groupnames)
statfnames = np.zeros ([ngroups, len(mnames)], dtype=np.dtype('a2000'))
for g in np.arange(ngroups):
group = groupnames[g]
for m in np.arange(len(mnames)):
measure = mnames[m]
statfnames[g,m] = outdir + group + '_' + measure + au.ext_str()
return [statfnames, mnames]
def get_stats_fnames(groupnames, outdir=''):
if np.ndim(groupnames) == 0:
groupnames = [groupnames]
if outdir:
outdir += outdir + os.path.sep
mnames = [au.sums_str(), au.mean_str(), au.var_str(), au.std_str()]
ngroups = len(groupnames)
statfnames = np.zeros([ngroups, len(mnames)], dtype=np.dtype('a2000'))
for g in np.arange(ngroups):
group = groupnames[g]
for m in np.arange(len(mnames)):
measure = mnames[m]
statfnames[g, m] = outdir + group + '_' + measure + au.ext_str()
return [statfnames, mnames]
def group_distance (measure_function, datadir, groups, groupsizes, chkf, absolute=False, outdir='', foldno='', expname='', exclude_idx=-1, exclude_subj='', exclude_subjclass=''):
olddir = os.getcwd()
if not outdir:
outdir = datadir
ngroups = len(groups)
#matrix of strings of 2000 characters maximum, to save filepaths
gfnames = np.zeros ([ngroups,3], dtype=np.dtype('a2000'))
subject_excluded = False
for g1 in range(ngroups):
g1name = groups[g1]
#mean1fname
gfnames[g1,0] = datadir + os.path.sep + g1name + '_' + au.mean_str()
#var1fname
gfnames[g1,1] = datadir + os.path.sep + g1name + '_' + au.var_str()
#std1fname
gfnames[g1,2] = datadir + os.path.sep + g1name + '_' + au.std_str()
for g2 in range(g1+1, ngroups):
g2name = groups[g2]
gfnames[g2,0] = datadir + os.path.sep + g2name + '_' + au.mean_str()
gfnames[g2,1] = datadir + os.path.sep + g2name + '_' + au.var_str()
gfnames[g2,2] = datadir + os.path.sep + g2name + '_' + au.std_str()
experiment = g1name + '_vs_' + g2name
#check if exclude_subjclass is any of both current groups
eg = -1
if exclude_idx > -1:
if exclude_subjclass == g1name: eg = g2
elif exclude_subjclass == g2name: eg = g1
step = au.measure_str() + ' ' + measure_function.func_name + ' ' + experiment + ' ' + datadir
#remove subject from stats
if eg > -1:
exclude_str = '_' + au.excluded_str() + str(exclude_idx)
step += exclude_str
experiment += exclude_str
if not au.is_done(chkf, step):
if not subject_excluded:
newmeanfname = gfnames[eg,0] + exclude_str
newvarfname = gfnames[eg,1] + exclude_str
newstdfname = gfnames[eg,2] + exclude_str
rstep = au.remove_str() + ' ' + au.subject_str() + ' ' + str(exclude_subj) + ' ' + au.fromstats_str() + ' ' + datadir
if not au.is_done(chkf, rstep):
#(meanfname, varfname, samplesize, subjvolfname, newmeanfname, newvarfname, newstdfname='')
remove_subject_from_stats (gfnames[eg,0], gfnames[eg,1], groupsizes[eg][1], exclude_subj, newmeanfname, newvarfname, newstdfname)
au.checklist_add (chkf, rstep)
gfnames[eg,0] += exclude_str
gfnames[eg,1] += exclude_str
gfnames[eg,2] += exclude_str
groupsizes[eg][1] -= 1
subject_excluded = True
#calculating distance
if not au.is_done(chkf, step):
mean1fname = au.add_extension_if_needed (gfnames[g1,0], au.ext_str())
mean2fname = au.add_extension_if_needed (gfnames[g2,0], au.ext_str())
var1fname = au.add_extension_if_needed (gfnames[g1,1], au.ext_str())
var2fname = au.add_extension_if_needed (gfnames[g2,1], au.ext_str())
std1fname = au.add_extension_if_needed (gfnames[g1,2], au.ext_str())
std2fname = au.add_extension_if_needed (gfnames[g2,2], au.ext_str())
outfname = measure_function (mean1fname, mean2fname, var1fname, var2fname, std1fname, std2fname, groupsizes[g1][1], groupsizes[g2][1], experiment, outdir, exclude_idx)
if absolute:
change_to_absolute_values (outfname)
au.checklist_add (chkf, step)
return outfname
def group_distance(measure_function,
datadir,
groups,
groupsizes,
chkf,
absolute=False,
outdir='',
foldno='',
expname='',
exclude_idx=-1,
exclude_subj='',
exclude_subjclass=''):
olddir = os.getcwd()
if not outdir:
outdir = datadir
ngroups = len(groups)
#matrix of strings of 2000 characters maximum, to save filepaths
gfnames = np.zeros([ngroups, 3], dtype=np.dtype('a2000'))
subject_excluded = False
for g1 in range(ngroups):
g1name = groups[g1]
#mean1fname
gfnames[g1, 0] = datadir + os.path.sep + g1name + '_' + au.mean_str()
#var1fname
gfnames[g1, 1] = datadir + os.path.sep + g1name + '_' + au.var_str()
#std1fname
gfnames[g1, 2] = datadir + os.path.sep + g1name + '_' + au.std_str()
for g2 in range(g1 + 1, ngroups):
g2name = groups[g2]
gfnames[g2,
0] = datadir + os.path.sep + g2name + '_' + au.mean_str()
gfnames[g2,
1] = datadir + os.path.sep + g2name + '_' + au.var_str()
gfnames[g2,
2] = datadir + os.path.sep + g2name + '_' + au.std_str()
experiment = g1name + '_vs_' + g2name
#check if exclude_subjclass is any of both current groups
eg = -1
if exclude_idx > -1:
if exclude_subjclass == g1name: eg = g2
elif exclude_subjclass == g2name: eg = g1
step = au.measure_str(
) + ' ' + measure_function.func_name + ' ' + experiment + ' ' + datadir
#remove subject from stats
if eg > -1:
exclude_str = '_' + au.excluded_str() + str(exclude_idx)
step += exclude_str
experiment += exclude_str
if not au.is_done(chkf, step):
if not subject_excluded:
newmeanfname = gfnames[eg, 0] + exclude_str
newvarfname = gfnames[eg, 1] + exclude_str
newstdfname = gfnames[eg, 2] + exclude_str
rstep = au.remove_str() + ' ' + au.subject_str(
) + ' ' + str(exclude_subj) + ' ' + au.fromstats_str(
) + ' ' + datadir
if not au.is_done(chkf, rstep):
#(meanfname, varfname, samplesize, subjvolfname, newmeanfname, newvarfname, newstdfname='')
remove_subject_from_stats(gfnames[eg,
0], gfnames[eg,
1],
groupsizes[eg][1],
exclude_subj,
newmeanfname,
newvarfname, newstdfname)
au.checklist_add(chkf, rstep)
gfnames[eg, 0] += exclude_str
gfnames[eg, 1] += exclude_str
gfnames[eg, 2] += exclude_str
groupsizes[eg][1] -= 1
subject_excluded = True
#calculating distance
if not au.is_done(chkf, step):
mean1fname = au.add_extension_if_needed(
gfnames[g1, 0], au.ext_str())
mean2fname = au.add_extension_if_needed(
gfnames[g2, 0], au.ext_str())
var1fname = au.add_extension_if_needed(gfnames[g1, 1],
au.ext_str())
var2fname = au.add_extension_if_needed(gfnames[g2, 1],
au.ext_str())
std1fname = au.add_extension_if_needed(gfnames[g1, 2],
au.ext_str())
std2fname = au.add_extension_if_needed(gfnames[g2, 2],
au.ext_str())
outfname = measure_function(mean1fname, mean2fname, var1fname,
var2fname, std1fname, std2fname,
groupsizes[g1][1],
groupsizes[g2][1], experiment,
outdir, exclude_idx)
if absolute:
change_to_absolute_values(outfname)
au.checklist_add(chkf, step)
return outfname