def check_probtrackx_complete(trackResDir,
mode,
doSeedNorm=True,
doSize=True,
logFN=None):
# Mode: seedOnly or seedTarg
import os
from scai_utils import check_file
ALL_MODES = ["seedOnly", "seedTarg"]
assert (ALL_MODES.count(mode) == 1)
fdt = os.path.join(trackResDir, "fdt_paths.nii.gz")
check_file(fdt, logFN=logFN)
if doSeedNorm:
fdt_norm = os.path.join(trackResDir, "fdt_paths_norm.nii.gz")
check_file(fdt_norm, logFN=logFN)
if doSize:
seed_size_fn = os.path.join(trackResDir, "seed_size.txt")
check_file(seed_size_fn, logFN=logFN)
if mode == "seedTarg":
targ_size_fn = os.path.join(targResDir, "targ_size.txt")
check_file(targ_size_fn, logFN=logFN)
def get_tracula_settings(cfgFN):
tracCfg = {}
from scai_utils import check_file, read_text_file
check_file(cfgFN)
txt = read_text_file(cfgFN)
bFoundPathList = False
for (i0, t_line) in enumerate(txt):
if t_line.strip().startswith("set pathlist ="):
bFoundPathList = True
break
paths = []
if bFoundPathList:
i1 = i0
ended = False
while not ended:
items = txt[i1].split(" ")
if len(items) == 0:
i1 += 1
continue
if items[-1] == "\\":
items = items[:-1]
ended = txt[i1].strip().endswith(")")
i1 += 1
for t_item in items:
t_item = t_item.replace("(", "").replace(")", "")
if t_item.startswith("lh.") or t_item.startswith("rh") or \
t_item.startswith("fmajor") or t_item.startswith("fminor") \
and len(t_item) > 6:
paths.append(t_item)
tracCfg["paths"] = paths
#print(txt)
return tracCfg
def get_tracula_settings(cfgFN):
tracCfg = {}
from scai_utils import check_file, read_text_file
check_file(cfgFN)
txt = read_text_file(cfgFN)
bFoundPathList = False
for (i0, t_line) in enumerate(txt):
if t_line.strip().startswith("set pathlist ="):
bFoundPathList = True
break
paths = []
if bFoundPathList:
i1 = i0
ended = False
while not ended:
items = txt[i1].split(" ")
if len(items) == 0:
i1 += 1
continue
if items[-1] == "\\":
items = items[:-1]
ended = txt[i1].strip().endswith(")")
i1 += 1
for t_item in items:
t_item = t_item.replace("(", "").replace(")", "")
if t_item.startswith("lh.") or t_item.startswith("rh") or \
t_item.startswith("fmajor") or t_item.startswith("fminor") \
and len(t_item) > 6:
paths.append(t_item)
tracCfg["paths"] = paths
#print(txt)
return tracCfg
def gen_parc_masks(rois, roiNums, parcVol, outDir,
doVolStats=True, redo=False, logFN=None):
import os
import numpy as np
from scipy.io import savemat as savemat
from scai_utils import check_bin_path, check_file, \
cmd_stdout, saydo, info_log, error_log
check_bin_path("fslstats", logFN=logFN)
volStats = {"roiName": [], "nVoxels": [], "mm3": []}
for (i0, roi) in enumerate(rois):
roiNum = roiNums[i0]
maskFN = os.path.join(outDir, "%s.diff.nii.gz" % roi)
binCmd = "mri_binarize --i %s --match %d --o %s" % \
(parcVol, roiNum, maskFN)
if not os.path.isfile(maskFN) or redo:
saydo(binCmd, logFN=logFN)
check_file(maskFN, logFN=logFN)
#= Volume stats =#
(so, se) = cmd_stdout("fslstats %s -V" % maskFN)
assert(len(se) == 0)
so = so.split(" ")
assert(len(so) >= 2)
volStats["nVoxels"].append(int(so[0]))
volStats["mm3"].append(float(so[1]))
volStats["roiName"].append(roi)
if doVolStats:
volStats["roiName"] = np.array(volStats["roiName"])
volStats["nVoxels"] = np.array(volStats["nVoxels"])
volStats["mm3"] = np.array(volStats["mm3"])
volStatsFN = os.path.join(outDir, "vol_stats.mat")
savemat(volStatsFN, volStats)
check_file(volStatsFN, logFN=logFN)
info_log("INFO: %s: Saved volume stats of the mask files at\n\t%s" \
% (gen_parc_masks.__name__, volStatsFN), logFN=logFN)
def generate_design_matrix(isAWS, matFN, bReverse=False):
# Input arguments:
# isAWS: vector of 0's and 1's
# matFN: output design matrix .mat file name
# bReverse: if AWS should be set to -1 and ANS to +1 (default: no)
if len(isAWS) < 2:
error_log("The design matrix cannot be generated because there are fewer than two subjects")
if not matFN.endswith(".mat"):
error_log("The input .mat file name %s has a wrong extension name" % matFN)
X_line = 'X = ['
for (i0, t_isAWS) in enumerate(isAWS):
t_x = (float(t_isAWS) * 2.0 - 1.0)
if bReverse:
X_line += "%.1f; " % -t_x
else:
X_line += "%.1f; " % t_x
X_line = X_line[:-2] + "];\n"
X_line += "X = [ones(%d , 1), X];\n" % len(isAWS)
X_line += "save('%s', 'X', '-v4');\n" % os.path.abspath(matFN)
print(X_line) # DEBUG
(t_path, t_fn) = os.path.split(os.path.abspath(matFN))
mScriptGenX = os.path.join(t_path, 'gen_%s' % t_fn.replace(".mat", ".m"))
mScriptGenX_f = open(mScriptGenX, "wt")
mScriptGenX_f.write(X_line)
mScriptGenX_f.close()
check_file(mScriptGenX)
matlabCmd = "%s -nosplash -nodesktop -r 'run %s; exit; '" % \
(MATLAB_BIN, mScriptGenX)
saydo(matlabCmd)
check_file(matFN)
def check_probtrackx_complete(trackResDir, mode, doSeedNorm=True, doSize=True,
logFN=None):
# Mode: seedOnly or seedTarg
import os
from scai_utils import check_file
ALL_MODES = ["seedOnly", "seedTarg"]
assert(ALL_MODES.count(mode) == 1)
fdt = os.path.join(trackResDir, "fdt_paths.nii.gz")
check_file(fdt, logFN=logFN)
if doSeedNorm:
fdt_norm = os.path.join(trackResDir, "fdt_paths_norm.nii.gz")
check_file(fdt_norm, logFN=logFN)
if doSize:
seed_size_fn = os.path.join(trackResDir, "seed_size.txt")
check_file(seed_size_fn, logFN=logFN)
if mode == "seedTarg":
targ_size_fn = os.path.join(targResDir, "targ_size.txt")
check_file(targ_size_fn, logFN=logFN)
def calculate_roi_tensor_measures(parcName, roiList, wmDepths, TENSOR_MEASURES,
HEMIS, dmriDir, annotDir, tensMeasMatFN,
logFileName):
#=== Load masks (gm and wm of different depths) ===#
import os
import nibabel as nb
import numpy as np
from scai_utils import check_file, check_dir, info_log, error_log
mask_shapes = []
nDepths = len(wmDepths)
roiNames = []
nzIdx = []
for i0 in range(nDepths):
nzIdx.append([])
bSpeech = []
parcDir = os.path.join(annotDir, parcName)
for (i0, wmDepth) in enumerate(wmDepths):
if wmDepth == -1:
parcTypeDir = os.path.join(parcDir, "gm")
info_log("Loading gray-matter masks")
else:
parcTypeDir = os.path.join(parcDir, "wm%dmm" % wmDepth)
info_log("Loading white-matter masks of %d-mm depth" \
% wmDepth)
for (i1, troi) in enumerate(roiList):
for (i2, hemi) in enumerate(HEMIS):
maskFN = os.path.join(parcTypeDir, \
"%s_%s.diff.nii.gz" % (hemi, troi[0]))
check_file(maskFN, logFN=logFileName)
t_img = nb.load(maskFN)
t_img_dat = t_img.get_data()
mask_shapes.append(np.shape(t_img_dat))
t_img_dat = np.ndarray.flatten(t_img_dat)
nzIdx[i0].append(np.nonzero(t_img_dat)[0])
if wmDepth == -1:
if troi[2] == 'N':
bSpeech.append(0)
else:
bSpeech.append(1)
roiNames.append("%s_%s" % (hemi, troi[0]))
#=== Check that the dimensions of all mask images match ===#
if len(np.unique(mask_shapes)) != 1:
error_log("Non-unique matrix size among the mask files", logFN=logFN)
#=== Load the dtifit_* files and extract the measures ===#
nROIs = len(roiNames)
assert (len(bSpeech) == nROIs)
tensMeas = {}
mask_shape = np.unique(mask_shapes)
check_dir(dmriDir, logFN=logFileName)
for (i0, measName) in enumerate(TENSOR_MEASURES):
tensMeas[measName] = np.zeros([nROIs, nDepths])
for (i1, t_depth) in enumerate(wmDepths):
if t_depth == -1:
info_log("Extracting tensor measure %s from gray matter" \
% measName)
else:
info_log(
"Extracting tensor measure %s from %d-mm deep white matter"
% (measName, t_depth))
assert (len(nzIdx[i1]) == nROIs)
for (i2, troi) in enumerate(roiNames):
measImg = os.path.join(dmriDir, \
"dtifit_%s.nii.gz" % measName)
check_file(measImg, logFN=logFileName)
t_img = nb.load(measImg)
if not list(mask_shape[0]) == list(np.shape(t_img)):
error_log("The diffusion tensor measure volume %s (%s) does not have a dimension that matches those of the mask files" \
% (measImg, measName))
t_img_dat = np.ndarray.flatten(t_img.get_data())
tensMeas[measName][i2, i1] = \
np.mean(t_img_dat[nzIdx[i1][i2]])
#=== Write data to file ===#
from scipy.io import savemat
res = {
"roiNames": roiNames,
"bSpeech": bSpeech,
"parcName": parcName,
"maskShape": mask_shape,
"wmDepths": wmDepths,
"tensMeas": tensMeas
}
savemat(tensMeasMatFN, res)
check_file(tensMeasMatFN, logFN=logFileName)
info_log(
"Tensor measures (%d types) and associated data were saved at: %s" %
(len(TENSOR_MEASURES), tensMeasMatFN),
logFN=logFileName)
def get_dpath_track_data(trackDir):
# List of data fields to extract
dataFlds = [
"Count", "Volume", "Len_Min", "Len_Max", "Len_Avg", "Len_Center",
"AD_Avg", "AD_Avg_Weight", "AD_Avg_Center", "RD_Avg", "RD_Avg_Weight",
"RD_Avg_Center", "MD_Avg", "MD_Avg_Weight", "MD_Avg_Center", "FA_Avg",
"FA_Avg_Weight", "FA_Avg_Center"
]
dat = {}
import os
from scai_utils import check_dir, check_file, read_text_file, info_log
check_dir(trackDir)
# Read from pathstats.overall.txt
pso = os.path.join(trackDir, "pathstats.overall.txt")
check_file(pso)
txt = read_text_file(pso)
for (i0, t_line) in enumerate(txt):
t_line = t_line.strip()
if t_line.startswith("#"):
continue
t_items = t_line.split(" ")
if len(t_items) == 2:
if dataFlds.count(t_items[0]) == 1:
dat[t_items[0]] = float(t_items[1])
# Check for completeness
for (i0, t_fld) in enumerate(dataFlds):
if not (t_fld in dat):
info_log("WARNING: could not find data field %s in file %s" % \
(t_fld, pso), bWarn=True)
# Read from pathstats.byvoxel.txt
vso = os.path.join(trackDir, "pathstats.byvoxel.txt")
check_file(vso)
txt = read_text_file(vso)
import numpy as np
bvDat = {}
cols = {}
# Determine the number of data points
npts = 0
lidx0 = -1
bFoundHeader = False
for (i0, t_line) in enumerate(txt):
t_line = t_line.strip()
if len(t_line) == 0:
continue
if not (t_line.startswith("#") or t_line.startswith("x")):
npts += 1
if lidx0 == -1:
lidx0 = i0
if t_line.startswith("x"):
bFoundHeader = True
t_items = t_line.split(" ")
cols["x"] = t_items.index("x")
cols["y"] = t_items.index("y")
cols["z"] = t_items.index("z")
cols["AD"] = t_items.index("AD")
cols["RD"] = t_items.index("RD")
cols["MD"] = t_items.index("MD")
cols["FA"] = t_items.index("FA")
cols["AD_Avg"] = t_items.index("AD_Avg")
cols["RD_Avg"] = t_items.index("RD_Avg")
cols["MD_Avg"] = t_items.index("MD_Avg")
cols["FA_Avg"] = t_items.index("FA_Avg")
if not bFoundHeader:
raise Exception, "Cannot find header column in file %s" % vso
txt = txt[lidx0:lidx0 + npts]
#print(txt)
# Allocate space
bvDat["x"] = np.zeros(npts)
bvDat["y"] = np.zeros(npts)
bvDat["z"] = np.zeros(npts)
bvDat["AD"] = np.zeros(npts)
bvDat["RD"] = np.zeros(npts)
bvDat["MD"] = np.zeros(npts)
bvDat["FA"] = np.zeros(npts)
bvDat["AD_Avg"] = np.zeros(npts)
bvDat["RD_Avg"] = np.zeros(npts)
bvDat["MD_Avg"] = np.zeros(npts)
bvDat["FA_Avg"] = np.zeros(npts)
keys = bvDat.keys()
for (i0, t_line) in enumerate(txt):
t_items = t_line.split(" ")
for t_fld in keys:
bvDat[t_fld][i0] = float(t_items[cols[t_fld]])
dat["byVoxel"] = bvDat
return dat
['adSTs', 'Temporal', 'N', []], \
['pvSTs', 'Temporal', 'N', []], \
['avSTs', 'Temporal', 'N', []], \
['aMTg', 'Temporal', 'N', []], \
['pMTg', 'Temporal', 'N', []], \
['pITg', 'Temporal', 'N', []], \
['aCGg', 'Cingulate', 'S', ["aCG"]], \
['midCGg', 'Cingulate', 'N', ["midCG"]], \
['pCGg', 'Cingulate', 'N', ["pCG"]], \
['OC', 'Occipital', 'N', []], \
['MTOg', 'Occipital', 'N', ["MTO"]], \
['ITOg', 'Occipital', 'N', ["ITO"]], \
['Lg', 'Occipital', 'N', ["LG"]], \
['pPHg', 'Parahippocampal', 'N', ["pPH"]], \
['aPHg', 'Parahippocampal', 'N', ["aPH"]], \
['SCC', 'Parahippocampal', 'N', []]]
if __name__ == "__main__":
from scipy.io import savemat
from scai_utils import check_file
dat = {"aROIs": [], "speechROIs": []}
for (i0, t_line) in enumerate(aROIs):
dat["aROIs"].append(t_line[0])
if t_line[2] == "S":
dat["speechROIs"].append(t_line[0])
savemat("aparc12.mat", dat)
check_file("aparc12.mat")
else:
error_log("Unrecognized subject ID prefix in %s" % subjIDs[-1])
info_log("Found %d subjects" % len(subjIDs))
if bAll:
info_log("Number of AWS = %d" % subjIsAWS.count(1))
info_log("Number of ANS = %d" % subjIsAWS.count(0))
# Determine if between-group comparison is to be performed
bGrpComp = not (subjIsAWS.count(1) == 0 or subjIsAWS.count(0) == 0)
if args.bVol:
#=== Prepare volumetric registered 4D ===#
cvConFNs = [] # Common-volume contrast files
check_file(VOL_TEMPLATE)
templateSuffix = os.path.split(VOL_TEMPLATE)[1]\
.replace(".nii.gz", "").replace(".mgz", "").replace(".nii", "")
else:
#=== Project the con values to the surface ===#
commSurfConFNs = {}
for hemi in HEMIS:
commSurfConFNs[hemi] = []
bNew = False
for sID in subjIDs:
#== Locate the con file ==#
L1Dir = os.path.join(args.batchBase, sID, "firstlevel_%s" % modelName)
check_dir(L1Dir)
conFN = os.path.join(L1Dir, "con_%.4d.img" % args.contrastNum)
SQL_SETTINGS_FN = "mega_sql_settings"
TOTAL_SCANS_TABLE_NAME = "Total Scans"
if __name__ == "__main__":
import os
import sys
import argparse
from scai_utils import info_log, check_file
ap = argparse.ArgumentParser(
description="Find subjects who participated in multiple studies")
#--- Read sql settings ---#
sqlSettingsFN = os.path.join(os.getenv("HOME"), SQL_SETTINGS_FN)
check_file(sqlSettingsFN)
sf = open(sqlSettingsFN, "rt")
settings = sf.read().replace("\r", "\n").split("\n")
sf.close()
sqlSettings = {
"SQL_SERVER": settings[0],
"DATABASE_NAME": settings[1],
"SQL_USER": settings[2],
"pw": settings[3]
}
#--- Get data from server ---#
import MySQLdb
db = MySQLdb.connect(host=sqlSettings["SQL_SERVER"],
sys.exit(0)
args = ap.parse_args()
studyIDs = args.studyIDs.split(",")
subjIDs = args.subjIDs.split(",")
if len(studyIDs) != len(subjIDs):
raise Exception, "Unequal number of entries in studyIDs and subjIDs"
if args.bVerbose:
info_log("# of subjects queried = %d" % len(subjIDs))
#=== Establish SQL server connection ===#
sqlSettingsFN = os.path.join(os.getenv("HOME"), SQL_SETTINGS_FN)
check_file(sqlSettingsFN)
sf = open(sqlSettingsFN, "rt")
settings = sf.read().replace("\r", "\n").split("\n")
sf.close()
sqlSettings = {"SQL_SERVER": settings[0],
"DATABASE_NAME": settings[1],
"SQL_USER": settings[2],
"pw": settings[3]}
masterCodes = get_subject_master_code(sqlSettings, studyIDs, subjIDs,
args.bVerbose)
for (i0, t_mc) in enumerate(masterCodes):
fsDataDir = machineSettings[hostName]["fsDataDir"]
sDataDir = os.path.join(dataDir, sID)
sBatchDataDir = os.path.join(batchDataDir, sID)
check_dir(dataDir)
check_dir(sDataDir)
check_dir(sBatchDataDir, bCreate=True)
check_dir(fsDataDir)
boldDir = os.path.join(sDataDir, "bold")
check_dir(boldDir)
#=== Look for the fmri_model.mat and fmri_contrasts.mat files ===#
modelMat = os.path.join(sDataDir, "fmri_model.mat")
check_file(modelMat)
info_log("modelMat = %s" % modelMat)
contrMat = os.path.join(sDataDir, "fmri_contrasts.mat")
check_file(contrMat)
info_log("contrMat = %s" % contrMat)
#=== Copy over the .mat files ===#
saydo("cp %s %s/" % (modelMat, sBatchDataDir))
saydo("cp %s %s/" % (contrMat, sBatchDataDir))
#=== Load model mat and determine how many runs were collected ===#
from scipy.io import loadmat
mdl = loadmat(modelMat)
nRuns = len(mdl["sess"][0])
def get_parc_stats(fsDir, subjID, parcName, bVerbose=False):
import sys, os
import tempfile
from scai_utils import (
check_dir,
check_file,
check_bin_path,
info_log,
cmd_stdout,
saydo,
read_text_file,
remove_empty_strings,
)
# === Constants ===#
hemis = ["lh", "rh"]
segStatsBin = "mri_segstats"
# === Implementation ===#
check_bin_path(segStatsBin)
sDir = os.path.join(fsDir, subjID)
check_dir(sDir)
lblDir = os.path.join(sDir, "label")
check_dir(sDir)
if bVerbose:
info_log("Label directory = %s" % lblDir)
morphInfo = {}
rois = []
area_mm2 = []
for (i0, hemi) in enumerate(hemis):
if bVerbose:
info_log("Working on hemisphere: %s" % hemi)
annot = os.path.join(lblDir, "%s.%s.annot" % (hemi, parcName))
check_file(annot)
tmpSum = tempfile.mktemp()
cmd = "%s --annot %s %s %s --sum %s" % (segStatsBin, subjID, hemi, parcName, tmpSum)
saydo(cmd)
check_file(tmpSum)
if bVerbose:
print("Intermediate results saved at: %s" % tmpSum)
t = read_text_file(tmpSum)
for (i0, tline) in enumerate(t):
tline = tline.strip()
if len(tline) == 0:
continue
elif tline.startswith("#"):
continue
t_items = remove_empty_strings(tline.split())
if len(t_items) != 5:
continue
t_roi = t_items[4]
if t_roi.lower() == "none":
continue
rois.append("%s_%s" % (hemi, t_roi))
area_mm2.append(float(t_items[3]))
saydo("rm -rf %s" % tmpSum)
morphInfo["rois"] = rois
morphInfo["area_mm2"] = area_mm2
return morphInfo
def generate_conn_mat(roiList,
sc_roiList,
parcTypeDir,
parcTracksDir,
hemi,
arg_bSpeech,
maskType,
connFN,
logFN=None):
import os
import sys
import numpy as np
import nibabel as nb
from scai_utils import check_file, check_dir, info_log, error_log
bSC = sc_roiList != None # Subcortical mode flag
# Process cortical ROIs (this is needed for both SC and C matrix types)
mask_shapes = []
roiNames = []
nzIdx = []
bSpeech = []
for (i0, troi) in enumerate(roiList):
targROI = troi[0]
maskFN = os.path.join(parcTypeDir, \
"%s_%s.diff.nii.gz" % (hemi, targROI))
check_file(maskFN, logFN=logFN)
t_img = nb.load(maskFN)
t_img_dat = t_img.get_data()
mask_shapes.append(np.shape(t_img_dat))
t_img_dat = np.ndarray.flatten(t_img_dat)
nzIdx.append(np.nonzero(t_img_dat)[0])
roiNames.append(troi[0])
if troi[2] == 'N':
bSpeech.append(0)
else:
bSpeech.append(1)
roiNames = np.array(roiNames)
bSpeech = np.array(bSpeech)
nzIdx = np.array(nzIdx)
if arg_bSpeech:
roiNames = roiNames[np.nonzero(bSpeech)[0]]
nzIdx = nzIdx[np.nonzero(bSpeech)[0]]
#print(roiNames) # DEBUG
#print(bSpeech) # DEBUG
# Process subcortical ROIs
if bSC:
parcSCDir = os.path.join(os.path.split(parcTypeDir)[0], "subcort")
check_dir(parcSCDir)
sc_roiNames = []
sc_nzIdx = []
for (i0, troi) in enumerate(sc_roiList):
if (hemi == "lh" and troi.startswith("Left-")) or \
(hemi == "rh" and troi.startswith("Right")):
sc_roiNames.append(troi)
maskFN = os.path.join(parcSCDir, \
"%s.diff.nii.gz" % (troi))
check_file(maskFN, logFN=logFN)
t_img = nb.load(maskFN)
t_img_dat = t_img.get_data()
mask_shapes.append(np.shape(t_img_dat))
t_img_dat = np.ndarray.flatten(t_img_dat)
sc_nzIdx.append(np.nonzero(t_img_dat)[0])
#print(sc_nzIdx[-1]) # DEBUG
#print(maskFN) # DEBUG
sc_roiNames = np.array(sc_roiNames)
sc_nzIdx = np.array(sc_nzIdx)
#print(sc_roiNames) # DEBUG
nROIs = len(roiNames)
assert (len(nzIdx) == nROIs)
if len(np.unique(mask_shapes)) != 1:
error_log("Non-unique matrix size among the mask files", logFN=logFN)
imgShape = np.unique(mask_shapes)[0]
if bSC:
nROIs_sc = len(sc_roiNames)
#=== Check the completion of seed-only probtrackx ===#
#=== and calculate the conn matrix ===#
if not bSC:
d1_roiNames = roiNames
d2_roiNames = roiNames
else:
d1_roiNames = sc_roiNames
d2_roiNames = np.array(list(sc_roiNames) + list(roiNames))
connMat = np.zeros([len(d1_roiNames), len(d2_roiNames)])
#print(d2_roiNames) # DEBUG
#print(len(connMat)) # DEBUG
#print(len(connMat[0])) # DEBUG
#print(parcTracksDir) # DEBUG
if bSC:
tmp_dir = os.path.split(parcTracksDir)[1]
parcTracksSCDir = os.path.split(os.path.split(parcTracksDir)[0])[0]
parcTracksSCDir = os.path.join(parcTracksSCDir, "tracks_sc", tmp_dir)
#print(parcTracksSCDir) # DEBUG
check_dir(parcTracksSCDir)
for (i0, troi) in enumerate(d1_roiNames):
seedROI = troi
if not bSC:
trackResDir = os.path.join(parcTracksDir,
"%s_%s_%s" % \
(hemi, seedROI, maskType))
else:
trackResDir = os.path.join(parcTracksSCDir, seedROI)
check_probtrackx_complete(trackResDir,
"seedOnly",
doSeedNorm=True,
doSize=True,
logFN=logFN)
fdt_norm = os.path.join(trackResDir, "fdt_paths_norm.nii.gz")
t_img = nb.load(fdt_norm)
t_img_dat = t_img.get_data()
assert (list(np.shape(t_img_dat)) == list(imgShape))
t_img_dat = np.ndarray.flatten(t_img_dat)
for (i1, troi1) in enumerate(d2_roiNames):
if not bSC:
connMat[i0, i1] = np.mean(t_img_dat[nzIdx[i1]])
else:
if i1 < nROIs_sc:
connMat[i0, i1] = np.mean(t_img_dat[sc_nzIdx[i1]])
else:
connMat[i0, i1] = np.mean(t_img_dat[nzIdx[i1 - nROIs_sc]])
#=== Make symmetric ===#
if not bSC:
connMat = 0.5 * (connMat + connMat.T)
#print(connMat) ## DEBUG
#=== Write result .mat file ===#
from scipy.io import savemat
if not bSC:
res = {"roiNames": roiNames, "connMat": connMat}
else:
res = {
"d1_roiNames": d1_roiNames,
"d2_roiNames": d2_roiNames,
"connMat": connMat
}
savemat(connFN, res)
print("connFN = " + connFN)
check_file(connFN, logFN=logFN)
info_log("Connectivity matrix and associated data were saved at: %s" \
% (connFN),
logFN=logFN)
def generate_conn_mat(roiList, sc_roiList,
parcTypeDir, parcTracksDir, hemi,
arg_bSpeech, maskType, connFN, logFN=None):
import os
import sys
import numpy as np
import nibabel as nb
from scai_utils import check_file, check_dir, info_log, error_log
bSC = sc_roiList != None # Subcortical mode flag
# Process cortical ROIs (this is needed for both SC and C matrix types)
mask_shapes = []
roiNames = []
nzIdx = []
bSpeech = []
for (i0, troi) in enumerate(roiList):
targROI = troi[0]
maskFN = os.path.join(parcTypeDir, \
"%s_%s.diff.nii.gz" % (hemi, targROI))
check_file(maskFN, logFN=logFN)
t_img = nb.load(maskFN)
t_img_dat = t_img.get_data()
mask_shapes.append(np.shape(t_img_dat))
t_img_dat = np.ndarray.flatten(t_img_dat)
nzIdx.append(np.nonzero(t_img_dat)[0])
roiNames.append(troi[0])
if troi[2] == 'N':
bSpeech.append(0)
else:
bSpeech.append(1)
roiNames = np.array(roiNames)
bSpeech = np.array(bSpeech)
nzIdx = np.array(nzIdx)
if arg_bSpeech:
roiNames = roiNames[np.nonzero(bSpeech)[0]]
nzIdx = nzIdx[np.nonzero(bSpeech)[0]]
#print(roiNames) # DEBUG
#print(bSpeech) # DEBUG
# Process subcortical ROIs
if bSC:
parcSCDir = os.path.join(os.path.split(parcTypeDir)[0], "subcort")
check_dir(parcSCDir)
sc_roiNames = []
sc_nzIdx = []
for (i0, troi) in enumerate(sc_roiList):
if (hemi == "lh" and troi.startswith("Left-")) or \
(hemi == "rh" and troi.startswith("Right")):
sc_roiNames.append(troi)
maskFN = os.path.join(parcSCDir, \
"%s.diff.nii.gz" % (troi))
check_file(maskFN, logFN=logFN)
t_img = nb.load(maskFN)
t_img_dat = t_img.get_data()
mask_shapes.append(np.shape(t_img_dat))
t_img_dat = np.ndarray.flatten(t_img_dat)
sc_nzIdx.append(np.nonzero(t_img_dat)[0])
#print(sc_nzIdx[-1]) # DEBUG
#print(maskFN) # DEBUG
sc_roiNames = np.array(sc_roiNames)
sc_nzIdx = np.array(sc_nzIdx)
#print(sc_roiNames) # DEBUG
nROIs = len(roiNames)
assert(len(nzIdx) == nROIs)
if len(np.unique(mask_shapes)) != 1:
error_log("Non-unique matrix size among the mask files", logFN=logFN)
imgShape = np.unique(mask_shapes)[0]
if bSC:
nROIs_sc = len(sc_roiNames)
#=== Check the completion of seed-only probtrackx ===#
#=== and calculate the conn matrix ===#
if not bSC:
d1_roiNames = roiNames
d2_roiNames = roiNames
else:
d1_roiNames = sc_roiNames
d2_roiNames = np.array(list(sc_roiNames) + list(roiNames))
connMat = np.zeros([len(d1_roiNames), len(d2_roiNames)])
#print(d2_roiNames) # DEBUG
#print(len(connMat)) # DEBUG
#print(len(connMat[0])) # DEBUG
#print(parcTracksDir) # DEBUG
if bSC:
tmp_dir = os.path.split(parcTracksDir)[1]
parcTracksSCDir = os.path.split(os.path.split(parcTracksDir)[0])[0]
parcTracksSCDir = os.path.join(parcTracksSCDir, "tracks_sc", tmp_dir)
#print(parcTracksSCDir) # DEBUG
check_dir(parcTracksSCDir)
for (i0, troi) in enumerate(d1_roiNames):
seedROI = troi
if not bSC:
trackResDir = os.path.join(parcTracksDir,
"%s_%s_%s" % \
(hemi, seedROI, maskType))
else:
trackResDir = os.path.join(parcTracksSCDir, seedROI)
check_probtrackx_complete(trackResDir, "seedOnly",
doSeedNorm=True, doSize=True,
logFN=logFN)
fdt_norm = os.path.join(trackResDir, "fdt_paths_norm.nii.gz")
t_img = nb.load(fdt_norm)
t_img_dat = t_img.get_data()
assert(list(np.shape(t_img_dat)) == list(imgShape))
t_img_dat = np.ndarray.flatten(t_img_dat)
for (i1, troi1) in enumerate(d2_roiNames):
if not bSC:
connMat[i0, i1] = np.mean(t_img_dat[nzIdx[i1]])
else:
if i1 < nROIs_sc:
connMat[i0, i1] = np.mean(t_img_dat[sc_nzIdx[i1]])
else:
connMat[i0, i1] = np.mean(t_img_dat[nzIdx[i1 - nROIs_sc]])
#=== Make symmetric ===#
if not bSC:
connMat = 0.5 * (connMat + connMat.T)
#print(connMat) ## DEBUG
#=== Write result .mat file ===#
from scipy.io import savemat
if not bSC:
res = {"roiNames": roiNames,
"connMat": connMat}
else:
res = {"d1_roiNames": d1_roiNames,
"d2_roiNames": d2_roiNames,
"connMat": connMat}
savemat(connFN, res)
print("connFN = " + connFN)
check_file(connFN, logFN=logFN)
info_log("Connectivity matrix and associated data were saved at: %s" \
% (connFN),
logFN=logFN)
def run_probtrackx(seedMask,
targMask,
bedpBase,
brainMask,
outDir,
doSeedNorm=True,
doSize=True,
doTargMaskedFDT=True,
ccStop=False,
bRedo=False,
logFN=None):
#=========================================================#
# Mode 1: from seed to targ
# Specify both seedMask and targMask
#
# Mode 2: from seed to all
# Specify only seedMask; set targMask=None
#
# Options:
# ccStop: Use corpus callosum stop mask
#
#=========================================================#
import os
from scai_utils import check_file, check_dir, check_bin_path, \
saydo, cmd_stdout, info_log, error_log
from mri_utils import nz_voxels
#== Get seed and targ nvox ==#
check_file(seedMask, logFN=logFN)
(seed_nVoxels, seed_mm3) = nz_voxels(seedMask)
seed_nVoxels = float(seed_nVoxels)
#assert(seed_nVoxels > 0)
if targMask != None:
check_file(targMask, logFN=logFN)
(targ_nVoxels, targ_mm3) = nz_voxels(targMask)
targ_nVoxels = float(targ_nVoxels)
assert (targ_nVoxels > 0)
check_bin_path("probtrackx", logFN=logFN)
check_dir(outDir, logFN=logFN)
if targMask != None:
#= Prepare waypoint file =#
wpfn = os.path.join(outDir, "waypoints.txt")
wptext = os.path.abspath(targMask) + "\n"
wpf = open(wpfn, "w")
wpf.write(wptext)
wpf.close()
check_file(wpfn, logFN=logFN)
cmd = 'probtrackx --mode=seedmask -x %s ' % seedMask + \
'-s %s ' % bedpBase + \
'-m %s ' % brainMask + \
'-l -c 0.2 -S 2000 --steplength=0.5 ' + \
'-P 5000 ' + \
'--forcedir --opd --pd --dir=%s ' % outDir
if targMask != None:
cmd += "--stop=%s --waypoints=%s " % (targMask, wpfn)
fdt_paths_fn = os.path.join(outDir, "fdt_paths.nii.gz")
#== Get the size of fdt_paths.nii.gz. If the size is zero, start over. ==#
if not os.path.isfile(fdt_paths_fn) \
or os.path.getsize(fdt_paths_fn) <= 1 \
or bRedo:
saydo(cmd, logFN=logFN)
#== Check for probtrackx completion ==#
check_file(fdt_paths_fn, logFN=logFN)
#== Save probtrackx command ==#
cmd_fn = os.path.join(outDir, "command.txt")
cmd_f = open(cmd_fn, "wt")
cmd_f.write("%s\n" % cmd)
cmd_f.close()
check_file(cmd_fn, logFN=logFN)
#== Generate seed size-normalized fdt_paths ==#
fdt_paths_norm_fn = os.path.join(outDir, "fdt_paths_norm.nii.gz")
check_bin_path("fslmaths", logFN=logFN)
norm_cmd = "fslmaths -dt float %s -div %d %s -odt float" % \
(fdt_paths_fn, seed_nVoxels, fdt_paths_norm_fn)
if not os.path.isfile(fdt_paths_norm_fn) or bRedo:
saydo(norm_cmd, logFN=logFN)
check_file(fdt_paths_norm_fn, logFN=logFN)
if doSize:
#== Write to seed size file ==#
seed_size_fn = os.path.join(outDir, 'seed_size.txt')
seed_size_f = open(seed_size_fn, 'w')
seed_size_f.write("%d %f" % (int(seed_nVoxels), seed_mm3))
seed_size_f.close()
check_file(seed_size_fn, logFN=logFN)
info_log("INFO: Saved seed size data to file: %s" % seed_size_fn,
logFN=logFN)
if targMask != None:
#== Write to targ size file ==#
targ_size_fn = os.path.join(outDir, 'targ_size.txt')
targ_size_f = open(targ_size_fn, 'w')
targ_size_f.write("%d %f" % (int(targ_nVoxels), targ_mm3))
targ_size_f.close()
check_file(targ_size_fn, logFN=logFN)
info_log("INFO: Saved targ size data to file: %s" % targ_size_fn,
logFN=logFN)
if (targMask != None) and doTargMaskedFDT:
#== Get target masked tract density ==#
check_bin_path("fslstats", logFN=logFN)
(so, se) = cmd_stdout("fslstats %s -k %s -m" \
% (fdt_paths_norm_fn, targMask))
assert (len(se) == 0)
so = so.split()
assert (len(so) >= 1)
targ_masked_norm_fdt = float(so[0])
targ_masked_norm_fdt_fn = \
os.path.join(outDir, "targ_masked_norm_fdt.txt")
tmnff = open(targ_masked_norm_fdt_fn, "wt")
tmnff.write("%f" % targ_masked_norm_fdt)
tmnff.close()
check_file(targ_masked_norm_fdt_fn, logFN=logFN)
info_log("INFO: Saved target-masked normalized FDT value tofile: %s" \
% targ_masked_norm_fdt_fn,
logFN=logFN)
imgFNs["lat"].append(os.path.join(CON_IMG_DIR,
"%s_%s_%s_%s.%s.lat.tiff" % \
(args.grp, args.contr, t_type,
grpCon, hemi)))
imgFNs["med"].append(os.path.join(CON_IMG_DIR,
"%s_%s_%s_%s.%s.med.tiff" % \
(args.grp, args.contr, t_type,
grpCon, hemi)))
if grpCon == "osgm":
sigImg = os.path.join(conDir, "%s_%s" % (grpCon, hemi),
grpCon, "sig.mgh")
elif grpCon == "bgc":
sigImg = os.path.join(conDir, "%s_%s" % (grpCon, hemi),
"con_01", "sig.mgh")
check_file(sigImg)
tksurferCommand = "tksurfer %s %s inflated -gray " % \
(commonSurfID, hemi, ) + \
"-overlay %s -fthresh %f -fmid %f " % \
(sigImg, args.fThresh, args.fMid) + \
"-colscalebarflag 1 "
# Prepare the tcl script
tclScript = tempfile.mktemp() + ".tcl"
t_imgFNs = []
t_imgFNs.append(imgFNs["lat"][-1])
t_imgFNs.append(imgFNs["med"][-1])
prep_tcl_script_surf(tclScript, t_imgFNs)
# Put in tcl script information
def calculate_roi_tensor_measures(parcName, roiList, wmDepths,
TENSOR_MEASURES, HEMIS,
dmriDir, annotDir,
tensMeasMatFN, logFileName):
#=== Load masks (gm and wm of different depths) ===#
import os
import nibabel as nb
import numpy as np
from scai_utils import check_file, check_dir, info_log, error_log
mask_shapes = []
nDepths = len(wmDepths)
roiNames = []
nzIdx = []
for i0 in range(nDepths):
nzIdx.append([])
bSpeech = []
parcDir = os.path.join(annotDir, parcName)
for (i0, wmDepth) in enumerate(wmDepths):
if wmDepth == -1:
parcTypeDir = os.path.join(parcDir, "gm")
info_log("Loading gray-matter masks")
else:
parcTypeDir = os.path.join(parcDir, "wm%dmm" % wmDepth)
info_log("Loading white-matter masks of %d-mm depth" \
% wmDepth)
for (i1, troi) in enumerate(roiList):
for (i2, hemi) in enumerate(HEMIS):
maskFN = os.path.join(parcTypeDir, \
"%s_%s.diff.nii.gz" % (hemi, troi[0]))
check_file(maskFN, logFN=logFileName)
t_img = nb.load(maskFN)
t_img_dat = t_img.get_data()
mask_shapes.append(np.shape(t_img_dat))
t_img_dat = np.ndarray.flatten(t_img_dat)
nzIdx[i0].append(np.nonzero(t_img_dat)[0])
if wmDepth == -1:
if troi[2] == 'N':
bSpeech.append(0)
else:
bSpeech.append(1)
roiNames.append("%s_%s" % (hemi, troi[0]))
#=== Check that the dimensions of all mask images match ===#
if len(np.unique(mask_shapes)) != 1:
error_log("Non-unique matrix size among the mask files",
logFN=logFN)
#=== Load the dtifit_* files and extract the measures ===#
nROIs = len(roiNames)
assert(len(bSpeech) == nROIs)
tensMeas = {}
mask_shape = np.unique(mask_shapes)
check_dir(dmriDir, logFN=logFileName)
for (i0, measName) in enumerate(TENSOR_MEASURES):
tensMeas[measName] = np.zeros([nROIs, nDepths])
for (i1, t_depth) in enumerate(wmDepths):
if t_depth == -1:
info_log("Extracting tensor measure %s from gray matter" \
% measName)
else:
info_log("Extracting tensor measure %s from %d-mm deep white matter" % (measName, t_depth))
assert(len(nzIdx[i1]) == nROIs)
for (i2, troi) in enumerate(roiNames):
measImg = os.path.join(dmriDir, \
"dtifit_%s.nii.gz" % measName)
check_file(measImg, logFN=logFileName)
t_img = nb.load(measImg)
if not list(mask_shape[0]) == list(np.shape(t_img)):
error_log("The diffusion tensor measure volume %s (%s) does not have a dimension that matches those of the mask files" \
% (measImg, measName))
t_img_dat = np.ndarray.flatten(t_img.get_data())
tensMeas[measName][i2, i1] = \
np.mean(t_img_dat[nzIdx[i1][i2]])
#=== Write data to file ===#
from scipy.io import savemat
res = {"roiNames": roiNames,
"bSpeech": bSpeech,
"parcName": parcName,
"maskShape": mask_shape,
"wmDepths": wmDepths,
"tensMeas": tensMeas}
savemat(tensMeasMatFN, res)
check_file(tensMeasMatFN, logFN=logFileName)
info_log("Tensor measures (%d types) and associated data were saved at: %s"
% (len(TENSOR_MEASURES), tensMeasMatFN),
logFN=logFileName)
WM_DEPTHS = [1, 2, 3]
# List of tensor measures to be extracted in steps including roi_tensor
# Need to match dtifit_*.nii.gz file names in dmri directory (generated by dtifit)
TENSOR_MEASURES = ["FA", "MD"]
SUBJECT_MASTER_CODE_FILE = "/speechlab/2/jtour/SID/Master_Code.xls"
FNIRT_TEMPLATE = "/speechlab/software/fsl64/data/standard/MNI152_T1_1mm_brain.nii.gz"
FNIRT_CNF = "/speechlab/5/scai/RHY/rhythm-fmri/fmri_code/MNI152_T1_1mm_brain.cnf"
SUBCORT_LABEL_LIST = "ASAP_subcortical_labels.txt"
SUBCORT_TRACT_SEEDS = ["Left-Thalamus-Proper", "Left-Caudate",
"Left-Putamen", "Left-Pallidum",
"Right-Thalamus-Proper", "Right-Caudate",
"Right-Putamen", "Right-Pallidum"]
if __name__ == "__main__":
from scipy.io import savemat
from scai_utils import check_file, info_log
matFN = __file__.replace(".py", ".mat")
analysisSettings = {"DWI_ANALYSIS_DIR": DWI_ANALYSIS_DIR,
"BASE_TRACULA_CFG": BASE_TRACULA_CFG,
"SUBJECT_MASTER_CODE_FILE": SUBJECT_MASTER_CODE_FILE}
savemat(matFN, analysisSettings)
check_file(matFN)
info_log("Saved analysis settings to mat file: %s" % matFN)
def get_dpath_track_data(trackDir):
# List of data fields to extract
dataFlds = ["Count", "Volume",
"Len_Min", "Len_Max", "Len_Avg", "Len_Center",
"AD_Avg", "AD_Avg_Weight", "AD_Avg_Center",
"RD_Avg", "RD_Avg_Weight", "RD_Avg_Center",
"MD_Avg", "MD_Avg_Weight", "MD_Avg_Center",
"FA_Avg", "FA_Avg_Weight", "FA_Avg_Center"]
dat = {}
import os
from scai_utils import check_dir, check_file, read_text_file, info_log
check_dir(trackDir)
# Read from pathstats.overall.txt
pso = os.path.join(trackDir, "pathstats.overall.txt")
check_file(pso)
txt = read_text_file(pso)
for (i0, t_line) in enumerate(txt):
t_line = t_line.strip()
if t_line.startswith("#"):
continue
t_items = t_line.split(" ")
if len(t_items) == 2:
if dataFlds.count(t_items[0]) == 1:
dat[t_items[0]] = float(t_items[1])
# Check for completeness
for (i0, t_fld) in enumerate(dataFlds):
if not (t_fld in dat):
info_log("WARNING: could not find data field %s in file %s" % \
(t_fld, pso), bWarn=True)
# Read from pathstats.byvoxel.txt
vso = os.path.join(trackDir, "pathstats.byvoxel.txt")
check_file(vso)
txt = read_text_file(vso)
import numpy as np
bvDat = {}
cols = {}
# Determine the number of data points
npts = 0
lidx0 = -1
bFoundHeader = False
for (i0, t_line) in enumerate(txt):
t_line = t_line.strip()
if len(t_line) == 0:
continue
if not (t_line.startswith("#") or t_line.startswith("x")):
npts += 1
if lidx0 == -1:
lidx0 = i0
if t_line.startswith("x"):
bFoundHeader = True
t_items = t_line.split(" ")
cols["x"] = t_items.index("x")
cols["y"] = t_items.index("y")
cols["z"] = t_items.index("z")
cols["AD"] = t_items.index("AD")
cols["RD"] = t_items.index("RD")
cols["MD"] = t_items.index("MD")
cols["FA"] = t_items.index("FA")
cols["AD_Avg"] = t_items.index("AD_Avg")
cols["RD_Avg"] = t_items.index("RD_Avg")
cols["MD_Avg"] = t_items.index("MD_Avg")
cols["FA_Avg"] = t_items.index("FA_Avg")
if not bFoundHeader:
raise Exception, "Cannot find header column in file %s" % vso
txt = txt[lidx0 : lidx0 + npts]
#print(txt)
# Allocate space
bvDat["x"] = np.zeros(npts)
bvDat["y"] = np.zeros(npts)
bvDat["z"] = np.zeros(npts)
bvDat["AD"] = np.zeros(npts)
bvDat["RD"] = np.zeros(npts)
bvDat["MD"] = np.zeros(npts)
bvDat["FA"] = np.zeros(npts)
bvDat["AD_Avg"] = np.zeros(npts)
bvDat["RD_Avg"] = np.zeros(npts)
bvDat["MD_Avg"] = np.zeros(npts)
bvDat["FA_Avg"] = np.zeros(npts)
keys = bvDat.keys()
for (i0, t_line) in enumerate(txt):
t_items = t_line.split(" ")
for t_fld in keys:
bvDat[t_fld][i0] = float(t_items[cols[t_fld]])
dat["byVoxel"] = bvDat
return dat
['pdSTs', 'Temporal', 'S', []], \
['adSTs', 'Temporal', 'N', []], \
['pvSTs', 'Temporal', 'N', []], \
['avSTs', 'Temporal', 'N', []], \
['aMTg', 'Temporal', 'N', []], \
['pMTg', 'Temporal', 'N', []], \
['pITg', 'Temporal', 'N', []], \
['aCGg', 'Cingulate', 'S', ["aCG"]], \
['midCGg', 'Cingulate', 'N', ["midCG"]], \
['pCGg', 'Cingulate', 'N', ["pCG"]], \
['OC', 'Occipital', 'N', []], \
['MTOg', 'Occipital', 'N', ["MTO"]], \
['ITOg', 'Occipital', 'N', ["ITO"]], \
['Lg', 'Occipital', 'N', ["LG"]], \
['pPHg', 'Parahippocampal', 'N', ["pPH"]], \
['aPHg', 'Parahippocampal', 'N', ["aPH"]], \
['SCC', 'Parahippocampal', 'N', []]]
if __name__ == "__main__":
from scipy.io import savemat
from scai_utils import check_file
dat = {"aROIs": [], "speechROIs": []}
for (i0, t_line) in enumerate(aROIs):
dat["aROIs"].append(t_line[0])
if t_line[2] == "S":
dat["speechROIs"].append(t_line[0])
savemat("aparc12.mat", dat)
check_file("aparc12.mat")
def run_probtrackx(seedMask, targMask, bedpBase, brainMask, outDir,
doSeedNorm=True, doSize=True,
doTargMaskedFDT=True,
ccStop=False,
bRedo=False,
logFN=None):
#=========================================================#
# Mode 1: from seed to targ
# Specify both seedMask and targMask
#
# Mode 2: from seed to all
# Specify only seedMask; set targMask=None
#
# Options:
# ccStop: Use corpus callosum stop mask
#
#=========================================================#
import os
from scai_utils import check_file, check_dir, check_bin_path, \
saydo, cmd_stdout, info_log, error_log
from mri_utils import nz_voxels
#== Get seed and targ nvox ==#
check_file(seedMask, logFN=logFN)
(seed_nVoxels, seed_mm3) = nz_voxels(seedMask)
seed_nVoxels = float(seed_nVoxels)
#assert(seed_nVoxels > 0)
if targMask != None:
check_file(targMask, logFN=logFN)
(targ_nVoxels, targ_mm3) = nz_voxels(targMask)
targ_nVoxels = float(targ_nVoxels)
assert(targ_nVoxels > 0)
check_bin_path("probtrackx", logFN=logFN)
check_dir(outDir, logFN=logFN)
if targMask != None:
#= Prepare waypoint file =#
wpfn = os.path.join(outDir, "waypoints.txt")
wptext = os.path.abspath(targMask) + "\n"
wpf = open(wpfn, "w")
wpf.write(wptext)
wpf.close()
check_file(wpfn, logFN=logFN)
cmd = 'probtrackx --mode=seedmask -x %s ' % seedMask + \
'-s %s ' % bedpBase + \
'-m %s ' % brainMask + \
'-l -c 0.2 -S 2000 --steplength=0.5 ' + \
'-P 5000 ' + \
'--forcedir --opd --pd --dir=%s ' % outDir
if targMask != None:
cmd += "--stop=%s --waypoints=%s " % (targMask, wpfn)
fdt_paths_fn = os.path.join(outDir, "fdt_paths.nii.gz")
#== Get the size of fdt_paths.nii.gz. If the size is zero, start over. ==#
if not os.path.isfile(fdt_paths_fn) \
or os.path.getsize(fdt_paths_fn) <= 1 \
or bRedo:
saydo(cmd, logFN=logFN)
#== Check for probtrackx completion ==#
check_file(fdt_paths_fn, logFN=logFN)
#== Save probtrackx command ==#
cmd_fn = os.path.join(outDir, "command.txt")
cmd_f = open(cmd_fn, "wt")
cmd_f.write("%s\n" % cmd)
cmd_f.close()
check_file(cmd_fn, logFN=logFN)
#== Generate seed size-normalized fdt_paths ==#
fdt_paths_norm_fn = os.path.join(outDir, "fdt_paths_norm.nii.gz")
check_bin_path("fslmaths", logFN=logFN)
norm_cmd = "fslmaths -dt float %s -div %d %s -odt float" % \
(fdt_paths_fn, seed_nVoxels, fdt_paths_norm_fn)
if not os.path.isfile(fdt_paths_norm_fn) or bRedo:
saydo(norm_cmd, logFN=logFN)
check_file(fdt_paths_norm_fn, logFN=logFN)
if doSize:
#== Write to seed size file ==#
seed_size_fn = os.path.join(outDir, 'seed_size.txt')
seed_size_f = open(seed_size_fn, 'w')
seed_size_f.write("%d %f" % (int(seed_nVoxels), seed_mm3))
seed_size_f.close()
check_file(seed_size_fn, logFN=logFN)
info_log("INFO: Saved seed size data to file: %s" % seed_size_fn,
logFN=logFN)
if targMask != None:
#== Write to targ size file ==#
targ_size_fn = os.path.join(outDir, 'targ_size.txt')
targ_size_f = open(targ_size_fn, 'w')
targ_size_f.write("%d %f" % (int(targ_nVoxels), targ_mm3))
targ_size_f.close()
check_file(targ_size_fn, logFN=logFN)
info_log("INFO: Saved targ size data to file: %s" % targ_size_fn,
logFN=logFN)
if (targMask != None) and doTargMaskedFDT:
#== Get target masked tract density ==#
check_bin_path("fslstats", logFN=logFN)
(so, se) = cmd_stdout("fslstats %s -k %s -m" \
% (fdt_paths_norm_fn, targMask))
assert(len(se) == 0)
so = so.split()
assert(len(so) >= 1)
targ_masked_norm_fdt = float(so[0])
targ_masked_norm_fdt_fn = \
os.path.join(outDir, "targ_masked_norm_fdt.txt")
tmnff = open(targ_masked_norm_fdt_fn, "wt")
tmnff.write("%f" % targ_masked_norm_fdt)
tmnff.close()
check_file(targ_masked_norm_fdt_fn, logFN=logFN)
info_log("INFO: Saved target-masked normalized FDT value tofile: %s" \
% targ_masked_norm_fdt_fn,
logFN=logFN)
