def measure_bhattacharyya_distance_fsl (mean1fname, mean2fname, \
var1fname, var2fname, \
std1fname, std2fname, \
numsubjs1, numsubjs2, \
experimentname, outdir, exclude_idx=-1):
if not os.path.exists(outdir):
os.mkdir(outdir)
olddir = os.getcwd()
os.chdir(outdir)
#following the equations:
#1/4 * ((m1-m2)^2/(var1+var2)) + 1/2 * (log( (var1+var2)/(2*std1*std2) ))
#from:
#1
#Bhattacharyya clustering with applications to mixture simplifications
#Frank Nielsen, Sylvain Boltz, and Olivier Schwander
#2010 International Conference on Pattern Recognition
#2
#The Divergence and Bhattacharyya Distance Measures in Signal Selection
#Kailath, T.
#http://dx.doi.org/10.1109/TCOM.1967.1089532
num1fname = outdir + os.path.sep + experimentname + '_bhatta_num1'
den1fname = outdir + os.path.sep + experimentname + '_bhatta_denom1'
bhatta1 = outdir + os.path.sep + experimentname + '_bhatta_first'
au.fslmaths(mean1fname + ' -sub ' + mean2fname + ' -sqr ' + num1fname)
au.fslmaths(var1fname + ' -add ' + var2fname + ' ' + den1fname)
au.fslmaths(num1fname + ' -div ' + den1fname + ' -div 4 ' + bhatta1)
num2fname = outdir + os.path.sep + experimentname + '_bhatta_num2'
au.fslmaths(var1fname + ' -add ' + var2fname + ' ' + num2fname)
den2fname = outdir + os.path.sep + experimentname + '_bhatta_denom2'
bhatta2 = outdir + os.path.sep + experimentname + '_bhatta_secnd'
au.fslmaths(std1fname + ' -mul ' + std2fname + ' -mul 2 ' + den2fname)
au.fslmaths(num2fname + ' -div ' + den2fname + ' -log -div 2 ' + bhatta2)
bhatta = outdir + os.path.sep + experimentname + '_' + au.bhattacharyya_str(
)
au.fslmaths(bhatta1 + ' -add ' + bhatta2 + ' ' + bhatta)
# au.imrm (num1fname)
# au.imrm (den1fname)
# au.imrm (bhatta1)
# au.imrm (num2fname)
# au.imrm (den2fname)
# au.imrm (bhatta2)
os.chdir(olddir)
return bhatta
def measure_bhattacharyya_distance_fsl (mean1fname, mean2fname, \
var1fname, var2fname, \
std1fname, std2fname, \
numsubjs1, numsubjs2, \
experimentname, outdir, exclude_idx=-1):
if not os.path.exists (outdir):
os.mkdir (outdir)
olddir = os.getcwd()
os.chdir (outdir)
#following the equations:
#1/4 * ((m1-m2)^2/(var1+var2)) + 1/2 * (log( (var1+var2)/(2*std1*std2) ))
#from:
#1
#Bhattacharyya clustering with applications to mixture simplifications
#Frank Nielsen, Sylvain Boltz, and Olivier Schwander
#2010 International Conference on Pattern Recognition
#2
#The Divergence and Bhattacharyya Distance Measures in Signal Selection
#Kailath, T.
#http://dx.doi.org/10.1109/TCOM.1967.1089532
num1fname = outdir + os.path.sep + experimentname + '_bhatta_num1'
den1fname = outdir + os.path.sep + experimentname + '_bhatta_denom1'
bhatta1 = outdir + os.path.sep + experimentname + '_bhatta_first'
au.fslmaths (mean1fname + ' -sub ' + mean2fname + ' -sqr ' + num1fname)
au.fslmaths (var1fname + ' -add ' + var2fname + ' ' + den1fname)
au.fslmaths (num1fname + ' -div ' + den1fname + ' -div 4 ' + bhatta1)
num2fname = outdir + os.path.sep + experimentname + '_bhatta_num2'
au.fslmaths (var1fname + ' -add ' + var2fname + ' ' + num2fname)
den2fname = outdir + os.path.sep + experimentname + '_bhatta_denom2'
bhatta2 = outdir + os.path.sep + experimentname + '_bhatta_secnd'
au.fslmaths (std1fname + ' -mul ' + std2fname + ' -mul 2 ' + den2fname)
au.fslmaths (num2fname + ' -div ' + den2fname + ' -log -div 2 ' + bhatta2)
bhatta = outdir + os.path.sep + experimentname + '_' + au.bhattacharyya_str()
au.fslmaths (bhatta1 + ' -add ' + bhatta2 + ' ' + bhatta)
# au.imrm (num1fname)
# au.imrm (den1fname)
# au.imrm (bhatta1)
# au.imrm (num2fname)
# au.imrm (den2fname)
# au.imrm (bhatta2)
os.chdir(olddir)
return bhatta
def measure_bhattacharyya_distance_py (mean1fname, mean2fname, \
var1fname, var2fname, \
std1fname, std2fname, \
numsubjs1, numsubjs2, \
experimentname, outdir, exclude_idx=-1):
if not os.path.exists(outdir):
os.mkdir(outdir)
#following the equations:
#1/4 * (m1-m2)^2/(var1+var2) + 1/2 * log( (var1+var2)/(2*std1*std2) )
#from:
#1
#Bhattacharyya clustering with applications to mixture simplifications
#Frank Nielsen, Sylvain Boltz, and Olivier Schwander
#2010 International Conference on Pattern Recognition
#2
#The Divergence and Bhattacharyya Distance Measures in Signal Selection
#Kailath, T.
#http://dx.doi.org/10.1109/TCOM.1967.1089532
aff = nib.load(mean1fname).get_affine()
m1 = nib.load(mean1fname).get_data()
m2 = nib.load(mean2fname).get_data()
v1 = nib.load(var1fname).get_data()
v2 = nib.load(var2fname).get_data()
s1 = nib.load(std1fname).get_data()
s2 = nib.load(std2fname).get_data()
b1 = 0.25 * (np.square(m1 - m2) / (v1 + v2)) + 0.5 * (np.log(
(v1 + v2) / (2 * s1 * s2)))
b1[np.isnan(b1)] = 0
b1[np.isinf(b1)] = 0
#b1 = np.nan_to_num(b1)
bhatta = outdir + os.path.sep + experimentname + '_' + au.bhattacharyya_str(
) + au.ext_str()
au.save_nibabel(bhatta, b1, aff)
return bhatta
def measure_bhattacharyya_distance_py (mean1fname, mean2fname, \
var1fname, var2fname, \
std1fname, std2fname, \
numsubjs1, numsubjs2, \
experimentname, outdir, exclude_idx=-1):
if not os.path.exists (outdir):
os.mkdir (outdir)
#following the equations:
#1/4 * (m1-m2)^2/(var1+var2) + 1/2 * log( (var1+var2)/(2*std1*std2) )
#from:
#1
#Bhattacharyya clustering with applications to mixture simplifications
#Frank Nielsen, Sylvain Boltz, and Olivier Schwander
#2010 International Conference on Pattern Recognition
#2
#The Divergence and Bhattacharyya Distance Measures in Signal Selection
#Kailath, T.
#http://dx.doi.org/10.1109/TCOM.1967.1089532
aff = nib.load(mean1fname).get_affine()
m1 = nib.load(mean1fname).get_data()
m2 = nib.load(mean2fname).get_data()
v1 = nib.load (var1fname).get_data()
v2 = nib.load (var2fname).get_data()
s1 = nib.load (std1fname).get_data()
s2 = nib.load (std2fname).get_data()
b1 = 0.25 * (np.square(m1 - m2) / (v1 + v2)) + 0.5 * (np.log((v1 + v2) / (2*s1*s2)))
b1[np.isnan(b1)] = 0
b1[np.isinf(b1)] = 0
#b1 = np.nan_to_num(b1)
bhatta = outdir + os.path.sep + experimentname + '_' + au.bhattacharyya_str() + au.ext_str()
au.save_nibabel(bhatta, b1, aff)
return bhatta
np.savetxt(outf_labels, subjlabels, fmt='%i')
#creating folder for slices
slidir = outdir + os.path.sep + au.slices_str()
if not (os.path.exists(slidir)):
os.mkdir(slidir)
#slice the volumes
#creating group and mask slices
pre.slice_and_merge(outf_subjs, outf_labels, chkf, outdir, maskf)
#creating measure output folder
if measure == 'pea':
measure_fname = au.pearson_str()
elif measure == 'bat':
measure_fname = au.bhattacharyya_str()
elif measure == 'ttest':
measure_fname = au.ttest_str()
#checking the leave parameter
if leave > (subjsnum - 1):
au.log.warning('aizkolari_measure: the leave (-l) argument value is ' +
str(leave) +
', bigger than the last index of subject: ' +
str(subjsnum - 1) + '. Im setting it to -1.')
leave = -1
#reading exclusion list
excluded = parse_exclude_list(excluf, leave)
#setting the output folder mdir extension
np.savetxt(outf_labels, subjlabels, fmt='%i')
#creating folder for slices
slidir = outdir + os.path.sep + au.slices_str()
if not(os.path.exists(slidir)):
os.mkdir(slidir)
#slice the volumes
#creating group and mask slices
pre.slice_and_merge(outf_subjs, outf_labels, chkf, outdir, maskf)
#creating measure output folder
if measure == 'pea':
measure_fname = au.pearson_str()
elif measure == 'bat':
measure_fname = au.bhattacharyya_str()
elif measure == 'ttest':
measure_fname = au.ttest_str()
#checking the leave parameter
if leave > (subjsnum - 1):
au.log.warning('aizkolari_measure: the leave (-l) argument value is ' + str(leave) + ', bigger than the last index of subject: ' + str(subjsnum - 1) + '. Im setting it to -1.')
leave = -1
#reading exclusion list
excluded = parse_exclude_list (excluf, leave)
#setting the output folder mdir extension
mdir = outdir + os.path.sep + measure_fname
if expname:
mdir += '_' + expname
