copy(outf_subjs, mdir) copy(outf_labels, mdir) #read the measure argument and start processing if measure == 'pea': if not stepdone: step = measureperslice_str() + ' ' + measure_fname + ' ' + mdir if not is_done(chkf, step): pear.aizkolari_data_pearson(outdir, mdir, usemask, excluf) checklist_add(chkf, step) step = postmerging_str() + ' ' + measure_fname + ' ' + mdir if not is_done(chkf, step): pearegex = pearson_str() + '_' + slice_str() + '*' post.merge_slices(mdir, pearegex, pearson_str(), mdir) checklist_add(chkf, step) elif measure == 'bat' or measure == 'ttest': if not stepdone: gsize = np.zeros([len(classnames), 2], dtype=int) for c in range(len(classnames)): gname = classnames[c] glabel = labels[c] godir = mdir + os.path.sep + gname print('Processing group ' + gname) gselect = np.zeros(len(subjs)) gsubjs = list() glabels = list()
outf_exclude = '' if (excluf): outf_exclude = au.exclude_str() if expname: outf_exclude += '_' + expname if foldnumber: outf_exclude += '_' + foldnumber np.savetxt(outdir + os.path.sep + outf_exclude , excluded, fmt='%i') np.savetxt(mdir + os.path.sep + au.exclude_str(), excluded, fmt='%i') excluf = mdir + os.path.sep + au.exclude_str() step = au.maskmerging_str() + ' ' + measure_fname + ' ' + mdir if usemask and not au.is_done(chkf, step): maskregex = au.mask_str() + '_' + au.slice_str() + '*' post.merge_slices (slidir, maskregex, au.mask_str(), mdir, False) au.checklist_add(chkf, step) #CORRELATION #read the measure argument and start processing if measure == 'pea': #measure pearson correlation for each population slice step = au.measureperslice_str() + step_params if not au.is_done(chkf, step): pear.pearson_correlation (outdir, mdir, usemask, excluf, leave) au.checklist_add(chkf, step) #merge all correlation slice measures step = au.postmerging_str() + step_params if not au.is_done(chkf, step): pearegex = au.pearson_str() + '_' + au.slice_str() + '*'
outf_exclude = au.exclude_str() if expname: outf_exclude += '_' + expname if foldnumber: outf_exclude += '_' + foldnumber np.savetxt(outdir + os.path.sep + outf_exclude, excluded, fmt='%i') np.savetxt(mdir + os.path.sep + au.exclude_str(), excluded, fmt='%i') excluf = mdir + os.path.sep + au.exclude_str() step = au.maskmerging_str() + ' ' + measure_fname + ' ' + mdir if usemask and not au.is_done(chkf, step): maskregex = au.mask_str() + '_' + au.slice_str() + '*' post.merge_slices(slidir, maskregex, au.mask_str(), mdir, False) au.checklist_add(chkf, step) #CORRELATION #read the measure argument and start processing if measure == 'pea': #measure pearson correlation for each population slice step = au.measureperslice_str() + step_params if not au.is_done(chkf, step): pear.pearson_correlation(outdir, mdir, usemask, excluf, leave) au.checklist_add(chkf, step) #merge all correlation slice measures step = au.postmerging_str() + step_params if not au.is_done(chkf, step): pearegex = au.pearson_str() + '_' + au.slice_str() + '*'
copy(outf_subjs, mdir) copy(outf_labels, mdir) #read the measure argument and start processing if measure == 'pea': if not stepdone: step = measureperslice_str() + ' ' + measure_fname + ' ' + mdir if not is_done(chkf, step): pear.aizkolari_data_pearson (outdir, mdir, usemask, excluf) checklist_add(chkf, step) step = postmerging_str() + ' '+ measure_fname + ' ' + mdir if not is_done(chkf, step): pearegex = pearson_str() + '_' + slice_str() + '*' post.merge_slices (mdir, pearegex, pearson_str(), mdir) checklist_add(chkf, step) elif measure == 'bat' or measure == 'ttest': if not stepdone: gsize = np.zeros([len(classnames),2], dtype=int) for c in range(len(classnames)): gname = classnames[c] glabel = labels [c] godir = mdir + os.path.sep + gname print ('Processing group ' + gname) gselect = np.zeros(len(subjs)) gsubjs = list() glabels = list()