args = ap.parse_args()
wmDepth = args.wmDepth
matter = args.matter
# === Determine the subject list and their group memberships ===
check_dir(BASE_DIR)
ds = glob.glob(os.path.join(BASE_DIR, "S??"))
ds.sort()
sIDs = []
isPWS = []
SSI4 = []
for (i0, t_path) in enumerate(ds):
(t_path_0, t_sID) = os.path.split(t_path)
sIDs.append(t_sID)
SSI4.append(get_qdec_info(t_sID, "SSI"))
if get_qdec_info(t_sID, "diagnosis") == "PWS":
isPWS.append(1)
else:
isPWS.append(0)
isPWS = np.array(isPWS)
assert(len(sIDs) > 0)
assert(len(sIDs) == len(isPWS))
# === Get the list of cortical ROIs ===
rois0 = get_aparc12_cort_rois(bSpeech=True)
check_file(CTAB)
assert(fwhm > 0)
# === Find subject IDs and group labels === #
check_dir(tractSegDir)
ds = glob.glob(os.path.join(tractSegDir, "*"))
ds.sort()
sIDs = []
bPWS = []
for (i0, t_fn) in enumerate(ds):
(t_path, t_sID) = os.path.split(t_fn)
sIDs.append(t_sID)
if get_qdec_info(t_sID, "diagnosis") == "PWS":
bPWS.append(1)
else:
bPWS.append(0)
sIDs = np.array(sIDs)
bPWS = np.array(bPWS)
# === Locate the d2a files of each subject === #
d2as = []
FAimgs = []
warps = []
for (i0, t_sID) in enumerate(sIDs):
t_d2a = os.path.join(TRACULA_DIR, t_sID, "dmri", "xfms", "d2a.mat")
check_file(t_d2a)
origDir = os.path.join(tbssDir, "origdata")
check_dir(origDir)
ds = glob.glob(os.path.join(origDir, "S??.nii.gz"))
ds.sort()
sIDs = []
idxPWS = []
idxPFS = []
for (i0, d) in enumerate(ds):
[tpath, tfn] = os.path.split(d)
sID = tfn.replace(".nii.gz", "")
sIDs.append(sID)
if get_qdec_info(sID, "diagnosis") == "PWS":
idxPWS.append(i0)
elif get_qdec_info(sID, "diagnosis") == "PFS":
idxPFS.append(i0)
else:
raise Exception, "Unrecognized diagnosis for subject %s: %s" % \
(sID, get_qdec_info(sID, "diagnosis"))
# === Process the clusters === #
for i0 in range(nClust):
# == Determine label == #
roi_fn = tempfile.mktemp() + ".nii.gz"
roi_cmd = "fslroi %s %s %d 1 %d 1 %d 1" % \
(atlas_label_fn, roi_fn, clustX[i0], clustY[i0], clustZ[i0])
saydo(roi_cmd)
check_file(roi_fn)
skipIDs = []
if args.skipSubj != None:
skipIDs = args.skipSubj.split(",")
sIDs = []
grps = []
for (i0, d) in enumerate(ds):
t_sID = os.path.split(d)[1]
if skipIDs.count(t_sID) > 0:
info_log("Skipping subject: %s" % t_sID)
continue
sIDs.append(t_sID)
grps.append(get_qdec_info(t_sID, "diagnosis"))
ugrps = list((np.unique(np.array(grps))))
ugrps.sort()
info_log("Discovered %s subjects" % len(sIDs))
info_log("The subjects belong to %d groups:" % (len(ugrps)))
for (i0, grp) in enumerate(ugrps):
info_log("\t%s" % grp)
matFile = __file__.replace(".py", ".mat")
from scipy.io import savemat, loadmat
if not os.path.isfile(matFile) or args.bRedo:
#=== Extract ROI morphological info ===#
morphInfo = []
d0 = glob.glob(os.path.join(inDir, "S??"))
sIDs = []
for (i0, t_d) in enumerate(d0):
sIDs.append(os.path.split(t_d)[1])
sIDs.sort()
grps = []
ages = []
genders = []
for (i0, t_sID) in enumerate(sIDs):
if get_qdec_info(t_sID, "diagnosis") == "PWS":
grps.append(1)
else:
grps.append(0)
if get_qdec_info(t_sID, "gender") == "Male":
genders.append(1)
else:
genders.append(0)
ages.append(float(get_qdec_info(t_sID, "Age")))
grps = np.array(grps)
ages = np.array(ages)
genders = np.array(genders)
d_orig = glob.glob(os.path.join(orig_dir, 'S*.nii.gz'))
S_list = []
for fn in d_orig:
S_list.append(fn.split('/')[-1].split('.')[0])
S_list.sort()
print('\nFound ' + str(len(S_list)) + ' subjects in ' + orig_dir + '\n')
# === Get subjects info ===
isPWS_list = [0] * len(S_list)
isFemale_list = [0] * len(S_list)
age_list = [-1.0] * len(S_list)
# done_list = [0] * len(S_list)
# SSI4_list = [-1.0] * len(S_list)
for (i0, sID) in enumerate(S_list):
t_diag = get_qdec_info(sID, "diagnosis")
if t_diag == "PWS":
isPWS_list[i0] = 1
else:
isPWS_list[i0] = 0
t_gen = get_qdec_info(sID, "gender")
if t_gen.lower() == "female":
isFemale_list[i0] = 1
else:
isFemale_list[i0] = 0
age_list[i0] = int(get_qdec_info(sID, "Age"))
isPWS_list = np.array(isPWS_list)
isFemale_list = np.array(isFemale_list)
args = ap.parse_args()
subjsListFN = args.subjsListFN
label = args.label
check_file(subjsListFN)
f_subjsList = open(subjsListFN, 'r')
t_subjs = f_subjsList.read().split('\n')
f_subjsList.close()
t_subjs = remove_empty_strings(t_subjs)
sIDs = {"PFS": [], "PWS": []}
for (i0, t_sID) in enumerate(t_subjs):
t_grp = get_qdec_info(t_sID, "diagnosis")
sIDs[t_grp].append(t_sID)
grps = sIDs.keys()
hemis = ["lh", "rh"]
asymmIdx = {}
for (i0, grp) in enumerate(grps):
asymmIdx[grp] = [np.nan] * len(sIDs[grp])
for (i1, sID) in enumerate(sIDs[grp]):
sDir = os.path.join(LABEL_BASE_DIR, sID)
check_dir(sDir)
vols = [0] * len(hemis)
for (j0, hemi) in enumerate(hemis):
diff_mask_fn = os.path.join(sDir, \
'PWS': np.array([np.nan] * nPWS)}
for grp in corr_z_tab.keys():
print('Processing data from group %s...'%grp)
for (i1, sID) in enumerate(sIDs[grp]):
print('Processing data from subject %s...'%sID)
res = np.load(os.path.join(bips_resting_dir, sID, resfn))
t_zs = np.matrix.reshape(res['corr_tab'], [1, nROIs * nROIs])[0]
# Fisher (z) transformation
corr_z_tab[grp][i1] = np.log((1 + t_zs) / (1 - t_zs)) * 0.5
# sys.exit(0)
if grp == 'PWS':
t_SSI = get_qdec_info(sID, 'SSI')
SSI4[i1] = float(t_SSI)
if len(corrMeas) > 0:
t_meas = get_qdec_info(sID, corrMeas)
try:
meas[grp][i1] = float(t_meas)
except:
print('\tWARNING: %s measure for %s is not valid'%(corrMeas, sID))
print('')
corr_z_tab[grp] = np.matrix.transpose(corr_z_tab[grp])
### Write to a clean mat file ###
idx_keep = []
for i0 in range(len(corr_z_tab['PFS'])):
ds.sort()
sIDs = []
isPWS = []
aparc12_fns = []
aparc12_diff_fns = []
merged = os.path.join(aparc12Dir, "merged.nii.gz")
os.system("rm -f %s" % merged)
merge_cmd = "fslmerge -t %s " % merged
for (i0, d) in enumerate(ds):
[tpath, tfn] = os.path.split(d)
sID = tfn.replace(".nii.gz", "")
sIDs.append(sID)
isPWS.append(get_qdec_info(sID, "diagnosis") == "PWS")
t_aparc12 = os.path.join(DATA_DIR, sID, "aparc12.nii.gz")
check_file(t_aparc12)
aparc12_fns.append(t_aparc12)
# == Locate the d2a FSL xfm mat == #
d2a = os.path.join(TRACULA_DIR, sID, "dmri", "xfms", "d2a.mat")
check_file(d2a)
# == Use convert_xfm to create a2d FSL xfm mat == #
a2d = os.path.join(TRACULA_DIR, sID, "dmri", "xfms", "a2d.mat")
os.system("rm -f %s" % a2d)
inv_cmd = "convert_xfm -omat %s -inverse %s" % (a2d, d2a)
saydo(inv_cmd)
check_file(a2d)