def pre_featsel (X, y, method, thr=95):
#pre feature selection, measuring distances
#Pearson correlation
if method == 'pearson':
au.log.info ('Calculating Pearson correlation')
m = np.abs(pearson_correlation (X, y))
#Bhattacharyya distance
elif method == 'bhattacharyya':
au.log.info ('Calculating Bhattacharyya distance')
m = bhattacharyya_dist (X, y)
#Welch's t-test
elif method == 'welcht':
au.log.info ("Calculating Welch's t-test")
m = welch_ttest (X, y)
#threshold data
if method != 'none':
mt = au.threshold_robust_range (m, thr)
return mt
def pre_featsel (X, y, method, thr=95):
#pre feature selection, measuring distances
#Pearson correlation
if method == 'pearson':
au.log.info ('Calculating Pearson correlation')
m = np.abs(pearson_correlation (X, y))
#Bhattacharyya distance
elif method == 'bhattacharyya':
au.log.info ('Calculating Bhattacharyya distance')
m = bhattacharyya_dist (X, y)
#Welch's t-test
elif method == 'welcht':
au.log.info ("Calculating Welch's t-test")
m = welch_ttest (X, y)
#threshold data
if method != 'none':
mt = au.threshold_robust_range (m, thr)
return mt
#mask process
au.log.info('Processing ' + maskf)
#loading mask and masking it with globalmask
mask = nib.load(maskf).get_data()
#thresholding
if absolute: mask = np.abs(mask)
if lthr: mask[mask < lthr] = 0
if uthr: mask[mask > uthr] = 0
if thrps:
for t in thrps:
au.log.info ("Thresholding " + maskf + " with robust range below " + str(t) + " percent.")
thrm = au.threshold_robust_range (mask, t)
au.log.info ("Extracting features.")
[feats, exclfeats, dmin, dmax] = extract_features (subjs, exclusubjs, thrm, maskf, scale, scale_min, scale_max)
au.log.info ("Saving data files.")
[filename, exclfilename] = save_data (outdir, prefix, dataname, otype, excluding, leave, feats, labels, exclfeats, exclulabels, dmin, dmax, scale, scale_min, scale_max, lthr, uthr, t, absolute)
else:
au.log.info ("Extracting features.")
[feats, exclfeats, dmin, dmax] = extract_features (subjs, exclusubjs, mask, maskf, scale, scale_min, scale_max)
au.log.info ("Saving data files.")
[filename, exclfilename] = save_data (outdir, prefix, dataname, otype, excluding, leave, feats, labels, exclfeats, exclulabels, dmin, dmax, scale, scale_min, scale_max, lthr, uthr, thrps, absolute)
au.log.info ("Saved " + filename)
au.setup_logger(args.verbosity)
mask = nib.load(maskf).get_data()
for i in ilst:
im = nib.load(i)
ivol = im.get_data()
ifnom = au.remove_ext(os.path.basename(imf))
if mask.shape != ivol.shape:
au.log.error ("Mask file " + maskf + " and input " + i + " do not have the same shape. Skipping...")
continue
for t in threshs:
au.log.info ("Thresholding " + i + " with " + str(t) + " robust lower bound.")
out = au.threshold_robust_range (ivol, t)
if odir:
of = odir
else:
of = os.path.dirname(i)
of += os.path.sep + ifnom + "_" + str(t) + "thrP" + ext
om = nib.Nifti1Image(out, im.get_affine(), im.get_header(), im.extra, im.file_map)
om.to_filename(of)
#-------------------------------------------------------------------------------
## END ROBUST THRESHOLDS
#-------------------------------------------------------------------------------
