Пример #1
0
            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() + '*'
                peameasf = mdir + os.path.sep + au.pearson_str()
Пример #2
0
      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() + '*'
            peameasf = mdir + os.path.sep + au.pearson_str()
Пример #3
0
def slice_and_merge (subsfname, labelsfname, checkfname, outdir='', maskfname='', outbase=''):

   check_data (subsfname, labelsfname, outdir, maskfname, outbase)

   nsubs = au.file_len(subsfname)
   nlabs = au.file_len(labelsfname)

   if not outdir:
      outdir = os.getcwd()

   slicesdir = outdir + os.path.sep + au.slices_str()
   tempdir = outdir + os.path.sep + au.temp_str()
   tmpdirlst = os.listdir(tempdir)

   fpath = ''
   olddir = os.getcwd()

   if not au.is_done (checkfname, au.preslicingdata_str()):
      #slicing subjects
      au.log.info ('Slicing all subjects: takes a while')
      subsfile = open(subsfname, 'r')
      for line in subsfile:
         fpath  = line.strip()
         isfile = os.path.basename(fpath)
         osfile = tempdir + os.path.sep + isfile

         #test if file has been sliced
         fdim3 = au.fslval(fpath, 'dim3')
         regex = au.remove_ext(isfile) + '*'
         nslices = au.count_match (tmpdirlst, regex)
         if fdim3 != nslices:
            #if not, then slice it in tempdir
            au.log.debug('Slicing ' + isfile)
            os.chdir (tempdir)

            shutil.copy (fpath, tempdir)
            au.fslslice (osfile)

            os.remove(osfile)
            os.chdir (olddir)
         else:
            au.log.debug(isfile + ' previously sliced')

      subsfile.close()
      au.checklist_add (checkfname, au.preslicingdata_str())

   if not au.is_done (checkfname, au.premergingdata_str()):
      #merging each slice
      if not outbase:
         outbase = au.data_str()

      if not fpath:
         subsfile = open(subsfname, 'r')
         for line in subsfile:
            fpath = line.strip()
            break

      au.log.info ('Merging all subject slices: takes a while')

      nslices  = int(au.fslval(fpath,'dim3'))
      for slice in range(nslices):
         slicezp = au.zeropad(slice)

         mergeout  = outbase + '_slice_' + slicezp
         outdata   = slicesdir + os.path.sep + mergeout
         #check if data file exists
         if not au.imtest(outdata):
            #if not, then create it, merging all the corresponding slices
            au.log.debug('Merging slice ' + slicezp)

            imglob   = ''
            subsfile = open(subsfname, 'r')
            for line in subsfile:
               fpath   = line.strip()
               isfile  = au.remove_ext(os.path.basename(fpath)).strip()
               isfile  = tempdir + os.path.sep + isfile + '_slice_' + slicezp
               imglob += isfile + ' '

            os.system('fslmerge -t ' + outdata + ' ' + imglob)

            for f in imglob.split():
               au.imrm(f)
         else:
            au.log.debug('Slice ' + slicezp + ' previously done')

         au.checklist_add (checkfname, au.premergingdata_str())

   #slicing mask
   if maskfname:
      if os.path.exists(maskfname) and not au.is_done (checkfname, au.preslicingmask_str()):
         au.log.info('Slicing mask ' + maskfname)

         au.imcp (maskfname, slicesdir + os.path.sep + au.mask_str())
         os.chdir (slicesdir)
         au.fslslice (au.mask_str())
         au.imrm     (au.mask_str())

         au.checklist_add (checkfname, au.preslicingmask_str())
         os.chdir (olddir)

   au.log.debug('Done preprocessing')
Пример #4
0
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
Пример #5
0
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