#checking number of files processed
nmasks = len(masklst)
nouts = 0
m = 0
for maskf in masklst:
if not scale:
ofname = au.features_str() + get_out_extension(otype)
else:
ofname = au.features_str() + '.' + au.scaled_str(
) + get_out_extension(otype)
if prefixes[m]:
ofname = prefixes[m] + '_' + ofname
oc = len(au.find(os.listdir(outdir), ofname))
nouts += oc
m += 1
if nouts >= nmasks:
au.log.debug('Nothing to do in ' + outdir + '. All files processed.')
return -1
else:
au.log.debug('Processing to output in: ' + outdir)
#number of subjects
subjsnum = au.file_len(subjsf)
#reading subjects list
subjlabels = np.zeros(subjsnum, dtype=int)
subjslist = {}
for tidx in tenum:
t = thrs[tidx]
for each subject
calculate_mean_without_subject
create_svmperf_file ()
perform_grid_search
perform_train
perform_test
save_results
try:
#in this case we will only have one trainset file per threshold
if scaled:
trainfeatsf = np.sort(au.find (os.listdir('.'), str(t) + 'thrP_features.scaled.svmperf'))[0]
testfeatsf = np.sort(au.find (os.listdir('.'), str(t) + 'thrP_excludedfeats.scaled.svmperf'))[0]
else:
trainfeatsf = np.sort(au.find (os.listdir('.'), str(t) + 'thrP_features.svmperf'))[0]
testfeatsf = np.sort(au.find (os.listdir('.'), str(t) + 'thrP_excludedfeats.svmperf'))[0]
except:
print ('Unexpected error: ' + str(sys.exc_info()))
print ('Failed looking for file ' + str(t) + 'thrP*.svmperf ' + ' in ' + os.getcwd())
exit(-1)
expname = m + '.' + d + '.' + str(t) + 'thr'
if not rocarea_opt:
expname += '.errorrate'
else:
expname += '.rocarea'
def do_darya_localizations ():
import scipy.io as sio
sys.path.append('/home/alexandre/Dropbox/Documents/work/visualize_volume')
import visualize_volume as vis
import aizkolari_utils as au
au.setup_logger()
locd = '/home/alexandre/Dropbox/ELM 2013/ELM-2013-Darya/localization'
wd, xd, dd, labelsf, phenof, dataf, masks, dilmasks, templates, pipe = get_filepaths()
maskf = dilmasks[0]
mask, hdr, aff = au.get_nii_data(maskf)
indices = np.array(np.where(mask > 0))
tmpltf = templates[0]
tmplt, _, _ = au.get_nii_data(tmpltf)
flist = os.listdir(locd)
flist = au.find(flist, '.mat')
flist.sort()
for f in flist:
data = sio.loadmat(os.path.join(locd, f))
name = au.remove_ext(f)
if f.find('cobre') >= 0:
p = data['pearson'].squeeze()
locs = p > 0
lidx = indices[:, locs].squeeze()
if f.find('reho') >= 0:
preho = p.copy()
elif f.find('alff') >= 0:
palff = p.copy()
else:
locs = data[name].squeeze()
locs -= 1
if f.find('pearson') >= 0:
if f.find('reho') >= 0:
lidx = indices[:, preho > 0]
elif f.find('alff') >= 0:
lidx = indices[:, palff > 0]
lidx = lidx[:, locs]
else:
lidx = indices[:, locs].squeeze()
locvol = np.zeros_like(mask, dtype=np.float)
locvol[tuple(lidx)] = 1
#save nifti volume
au.save_nibabel (os.path.join(locd, name + '.nii.gz'), locvol, aff, hdr)
#save image
fig = vis.show_many_slices(tmplt, locvol, volaxis=1, vol2_colormap=plt.cm.autumn, figtitle=name)
aizc.save_fig_to_png(fig, os.path.join(locd, name + '.png'))
if os.access (os.path.join(xd,'innercrop'), os.X_OK):
au.exec_command (os.path.join(xd,'innercrop') + ' -o white ' + fig2name + ' ' + fig2name)
tstdir = cvdir + os.path.sep + d + '_' + i
outdir = tstdir
ofname = outdir + os.path.sep + ofname
os.chdir(tstdir)
au.log.debug('cd ' + tstdir)
for tidx in tenum:
t = thrs[tidx]
try:
#in this case we will only have one trainset file per threshold
if scaled:
trainfeatsf = np.sort(
au.find(os.listdir('.'),
str(t) +
'thrP_features.scaled.svmperf'))[0]
testfeatsf = np.sort(
au.find(
os.listdir('.'),
str(t) +
'thrP_excludedfeats.scaled.svmperf'))[0]
else:
trainfeatsf = np.sort(
au.find(os.listdir('.'),
str(t) + 'thrP_features.svmperf'))[0]
testfeatsf = np.sort(
au.find(os.listdir('.'),
str(t) + 'thrP_excludedfeats.svmperf'))[0]
except:
au.log.error('Unexpected error: ' + str(sys.exc_info()))
au.setup_logger(verbose)
# checking number of files processed
nmasks = len(masklst)
nouts = 0
m = 0
for maskf in masklst:
if not scale:
ofname = au.features_str() + get_out_extension(otype)
else:
ofname = au.features_str() + "." + au.scaled_str() + get_out_extension(otype)
if prefixes[m]:
ofname = prefixes[m] + "_" + ofname
oc = len(au.find(os.listdir(outdir), ofname))
nouts += oc
m += 1
if nouts >= nmasks:
au.log.debug("Nothing to do in " + outdir + ". All files processed.")
return -1
else:
au.log.debug("Processing to output in: " + outdir)
# number of subjects
subjsnum = au.file_len(subjsf)
# reading subjects list
subjlabels = np.zeros(subjsnum, dtype=int)
subjslist = {}
