Remove obsolete code

code/localizerstats2mask.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-# Take all localizer 1st-level GLM analysis results, computes and average zstat map
-# for both contrasts of interest, masks them with the given mask image, and thresholds to
-# identify the voxels matching the desired percentile of largest values
-#
-# Usage:
-#   code/localizerstats2mask.sh sub-4 VT_roi 90
-
-set -e
-set -u
-
-sub=$1
-mask=$2
-percentile=$3
-dsdir="$(readlink -f .)"
-
-wdir=$(mktemp -d --suffix=localizer2mask)
-trap "rm -rf $wdir" SIGINT SIGTERM
-cd "$wdir"
-
-# define contrast names
-contrasts=(dummy all ffa ppa eba loc vis ffa_alt ppa_alt eba_alt loc_alt)
-
-##combine MNI2T1 and T12BOLD
-#$FSLDIR/bin/convert_xfm \
-#  -omat mni2bold.mat \
-#  -concat ${dsdir}/src/tnt/${sub}/t1w/in_bold/xfm_6dof.mat \
-#  ${dsdir}/src/tnt/${sub}/t1w/in_mni152/tmpl2subj.mat
-
-## re-slice mask
-#$FSLDIR/bin/flirt \
-#  -in "${dsdir}/${mask}" \
-#  -ref ${dsdir}/src/tnt/${sub}/bold/brain \
-#  -applyxfm -init mni2bold.mat \
-#  -out mask \
-#  -interp nearestneighbour
-
-## apply transformation to subject template for all runs
-#for f in ${dsdir}/${sub}/run-*.feat; do
-#  $FSLDIR/bin/featregapply "$f"
-#  # make zstats
-#  $FSLDIR/bin/fslmaths $f/reg_standard/stats/varcope1 -sqrt $f/reg_standard/stats/stdcope1
-#  $FSLDIR/bin/fslmaths $f/reg_standard/stats/varcope2 -sqrt $f/reg_standard/stats/stdcope2
-#  $FSLDIR/bin/fslmaths $f/reg_standard/stats/cope1 -div $f/reg_standard/stats/stdcope1 $f/reg_standard/stats/zstat1
-#  $FSLDIR/bin/fslmaths $f/reg_standard/stats/cope2 -div $f/reg_standard/stats/stdcope2 $f/reg_standard/stats/zstat2
-#done
-
-# make average zstat across all runs -- mask
-for i in $(seq 10); do
-  $FSLDIR/bin/fslmerge -t zstat${i}_merged ${dsdir}/${sub}/run-*.feat/stats/zstat${i}.nii*
-  #$FSLDIR/bin/fslmaths zstat${i}_merged -Tmean -mas mask zstat${i}
-  $FSLDIR/bin/fslmaths zstat${i}_merged -Tmean zstat${i}
-  # take desired upper percentile and threshold for mask
-  fslmaths zstat${i} \
-    -thr $($FSLDIR/bin/fslstats zstat${i} -P "$percentile") \
-    ${dsdir}/${sub}/${contrasts[$i]}_thr${percentile}
-done
-
-cd -
-rm -rf "$wdir"
-

code/localizerstats2mask.submit

@@ -1,42 +0,0 @@
-universe = vanilla
-output = condor_logs/$(CLUSTER).$(PROCESS).out
-error = condor_logs/$(CLUSTER).$(PROCESS).err
-log = condor_logs/$(CLUSTER).$(PROCESS).log
-getenv = True
-request_cpus = 1
-request_memory = 1000
-should_transfer_files = NO
-transfer_executable = False
-initialdir = /home/data/psyinf/forrest_gump/collection/visloc
-executable = code/localizerstats2mask.sh
-
-arguments = sub-01 dummy 99.9
-queue
-arguments = sub-02 dummy 99.9
-queue
-arguments = sub-03 dummy 99.9
-queue
-arguments = sub-04 dummy 99.9
-queue
-arguments = sub-05 dummy 99.9
-queue
-arguments = sub-06 dummy 99.9
-queue
-arguments = sub-09 dummy 99.9
-queue
-arguments = sub-10 dummy 99.9
-queue
-arguments = sub-14 dummy 99.9
-queue
-arguments = sub-15 dummy 99.9
-queue
-arguments = sub-16 dummy 99.9
-queue
-arguments = sub-17 dummy 99.9
-queue
-arguments = sub-18 dummy 99.9
-queue
-arguments = sub-19 dummy 99.9
-queue
-arguments = sub-20 dummy 99.9
-queue

code/stats2rois

@@ -1,235 +0,0 @@
-#!/usr/bin/python3
-
-import sys
-import os
-from tempfile import mkdtemp
-from os.path import join as _opj
-from os.path import abspath
-from shutil import rmtree
-from configparser import SafeConfigParser
-from subprocess import check_call
-import nibabel as nb
-from scipy.ndimage import label, center_of_mass, maximum_position
-import numpy as np
-
-
-def ijk2xyz(ijk, aff):
-    tmp = np.ones(4, dtype=ijk.dtype)
-    tmp[:3] = ijk
-    tmp = np.dot(aff, tmp)
-    return tmp[:3]
-
-
-cfg = SafeConfigParser()
-cfg.read(_opj('code', 'stats2rois.cfg'))
-
-subj = int(sys.argv[1])
-subj_code = 'sub-%.2i' % subj
-mode = None
-rois = None
-if len(sys.argv) > 2:
-    mode = 'indi'
-    rois = sys.argv[2:]
-
-if not os.path.exists(_opj(subj_code, 'rois')):
-    os.makedirs(_opj(subj_code, 'rois'))
-
-# encode ROI IDs as powers of 2
-roi_ids = {
-    'rFFA':  int('0000000000001', base=2),  # 1
-    'rOFA':  int('0000000000010', base=2),  # 2
-    'rpSTS': int('0000000000100', base=2),  # 4
-    'rPPA':  int('0000000001000', base=2),  # 8
-    'rEBA':  int('0000000010000', base=2),  # 16
-    'rLOC':  int('0000000100000', base=2),  # 32
-    'lFFA':  int('0000001000000', base=2),  # 64
-    'lOFA':  int('0000010000000', base=2),  # 128
-    'lpSTS': int('0000100000000', base=2),  # 256
-    'lPPA':  int('0001000000000', base=2),  # 512
-    'lEBA':  int('0010000000000', base=2),  # 1024
-    'lLOC':  int('0100000000000', base=2),  # 2048
-    'VIS':   int('1000000000000', base=2),  # 4096
-}
-
-#mask_fname = abspath(_opj(
-#    # TODO: switch back before release
-#    #'src',
-#    '..',
-#    'tnt',
-#    subj_code,
-#    'bold3Tp2',
-#    'brain_mask.nii.gz'))
-
-tmplwarp_fname = abspath(_opj(
-    # TODO: switch back before release
-    #'src',
-    '..',
-    'tnt',
-    subj_code,
-    'bold3Tp2',
-    'in_grpbold3Tp2',
-    'subj2tmpl_warp.nii.gz'))
-
-tmpl_fname = abspath(_opj(
-    # TODO: switch back before release
-    #'src',
-    '..',
-    'tnt',
-    'templates',
-    'grpbold3Tp2',
-    'brain.nii.gz'))
-
-roisummary_vol = None
-roisummary_hdr = None
-
-if rois is None:
-    rois = cfg.options(subj_code)
-for roi in rois:
-    # doesn't do casing properly
-    roi = roi[:-3] + roi[-3:].upper()
-    print(roi)
-    wdir = mkdtemp(suffix='_stats2roi_%s' % subj_code)
-    #wdir = '/tmp/stats2rois'
-    #print(wdir)
-    # get particular setting for this ROI
-    info = cfg.get(subj_code, roi).split()
-    cope = int(info[0])
-    zthresh = float(info[1])
-    clabels = info[2:]
-    cluster_idx = [int(i) for i in info[2:]]
-
-    zstats_fname = abspath(_opj(
-        subj_code,
-        '2ndlvl_z164.gfeat',
-        'cope%i.feat' % cope,
-        'thresh_zstat1.nii.gz'))
-    grp_zstats_fname = _opj(wdir, 'grpzstats.nii.gz')
-
-    # find clusters
-    zstat_img = nb.load(zstats_fname)
-    clusters, nclusters = label(zstat_img.get_data() > zthresh)
-    #print('Cluster sizes:', {i: np.sum(clusters == i) for i in range(1, nclusters + 1)})
-
-    # make ROI mask
-    roi_fname = _opj(subj_code, 'rois', '%s.nii.gz' % roi)
-    roi_vol = np.zeros(clusters.shape, dtype=np.bool)
-    for cli in cluster_idx:
-        roi_vol += clusters == cli
-    if roisummary_vol is None:
-        roisummary_vol = np.zeros(roi_vol.shape, dtype='int16')
-        roisummary_hdr = zstat_img.get_header()
-    roisummary_vol += roi_vol.astype(int) * roi_ids[roi]
-
-    # save the ROI mask file
-    roi_img = nb.Nifti1Image(roi_vol.astype('int16'), zstat_img.get_affine())
-    nb.save(roi_img, roi_fname)
-    with open(_opj(subj_code, 'rois', '%s_info.txt' % roi), 'w') as infofile:
-        # native space info
-        infofile.write("nvoxels (native): %i\n" % np.sum(roi_vol > 0))
-        infofile.write(
-            "volume (native cm^3): %f\n"
-            % (np.sum(roi_vol > 0) * np.prod(roi_img.get_header().get_zooms()) / 1000.))
-        roi_stats = zstat_img.get_data()[roi_vol > 0]
-        infofile.write(
-            'Z-stats (min, mean, median, max): %.2f, %.2f, %.2f, %.2f\n'
-            % (roi_stats.min(), roi_stats.mean(), np.median(roi_stats), roi_stats.max()))
-        roi_stats = zstat_img.get_data() * (roi_vol > 0)
-        infofile.write("peak Z (native IJK): %i, %i, %i\n" % maximum_position(roi_stats))
-        infofile.write("CoM (native IJK): %i, %i, %i\n" % center_of_mass(roi_stats))
-
-        # MNI space info
-        roi_stats_fname = _opj(wdir, 'roi_stats.nii.gz')
-        nb.save(nb.Nifti1Image(roi_stats, zstat_img.get_affine()), roi_stats_fname)
-        grp_stats_fname = _opj(wdir, 'grp_stats.nii.gz')
-        check_call([
-            'applywarp',
-            '--in=%s' % roi_stats_fname,
-            '--ref=%s' % tmpl_fname,
-            '--warp=%s' % tmplwarp_fname,
-            '--interp=trilinear',
-            '--out=%s' % grp_stats_fname])
-        grp_stats_img = nb.load(grp_stats_fname)
-        infofile.write(
-            "peak Z (MNI mm): %.2f, %.1f, %.1f\n"
-            % tuple(
-                ijk2xyz(
-                    np.array(maximum_position(grp_stats_img.get_data())),
-                    grp_stats_img.get_affine())))
-        infofile.write(
-            "CoM (MNI mm): %.2f, %.1f, %.1f\n"
-            % tuple(
-                ijk2xyz(
-                    np.array(center_of_mass(grp_stats_img.get_data())),
-                    grp_stats_img.get_affine())))
-
-    native_clusters_fname = _opj(wdir, 'native_clusters.nii.gz')
-    nb.save(zstat_img, _opj(wdir, 'stats.nii.gz'))
-    grp_roi_fname = _opj(wdir, 'grp_roi.nii.gz')
-    grp_clusters_fname = _opj(wdir, 'grp_clusters.nii.gz')
-    nb.save(nb.Nifti1Image(clusters, zstat_img.get_affine()),
-            native_clusters_fname)
-
-    #check_call(
-    #    ['easythresh', zstats_fname, mask_fname, '%s' % zthresh, '%s' % pthresh,
-    #     mask_fname, 'stats2rois'],
-    #    cwd=wdir)
-
-    if mode == 'indi':
-        check_call([
-            'applywarp',
-            '--in=%s' % roi_fname,
-            '--ref=%s' % tmpl_fname,
-            '--warp=%s' % tmplwarp_fname,
-            '--interp=nn',
-            '--out=%s' % grp_roi_fname])
-        check_call([
-            'applywarp',
-            '--in=%s' % native_clusters_fname,
-            '--ref=%s' % tmpl_fname,
-            '--warp=%s' % tmplwarp_fname,
-            '--interp=nn',
-            '--out=%s' % grp_clusters_fname])
-        check_call([
-            'applywarp',
-            '--in=%s' % zstats_fname,
-            '--ref=%s' % tmpl_fname,
-            '--warp=%s' % tmplwarp_fname,
-            '--interp=trilinear',
-            '--out=%s' % grp_zstats_fname])
-
-        check_call([
-            'fslview',
-            '../tnt/templates/grpbold3Tp2/from_mni/MNI152_T1_1mm.nii.gz',
-            grp_zstats_fname,
-            '-l', 'Red-Yellow',
-            grp_clusters_fname,
-            '-l',
-            'Random-Rainbow',
-            grp_roi_fname,
-            '-l',
-            'Greyscale',
-        ])
-
-    rmtree(wdir)
-
-if mode == 'indi':
-    sys.exit(0)
-
-roisummary_fname = _opj(subj_code, 'rois', 'summary.nii.gz')
-nb.save(nb.Nifti1Image(roisummary_vol, roisummary_hdr.get_best_affine()),
-        roisummary_fname)
-#grp_roisummary_fname = 'grp_roisummary.nii.gz'
-#check_call([
-#    'applywarp',
-#    '--in=%s' % roisummary_fname,
-#    '--ref=%s' % tmpl_fname,
-#    '--warp=%s' % tmplwarp_fname,
-#    '--interp=nn',
-#    '--out=%s' % grp_roisummary_fname])
-#check_call([
-#    'fslview',
-#    '../tnt/templates/grpbold3Tp2/from_mni/MNI152_T1_1mm.nii.gz',
-#    grp_roisummary_fname,
-#    '-l',
-#    'Random-Rainbow',
-#])