def format_bvals_bvecs(bvalsFN, bvecsFN, logFN=None):
from scai_utils import cmd_stdout, info_log, error_log
import numpy as np
#=== Check the format of bvals and bvecs ===#
import numpy as np
(so, se) = cmd_stdout("wc -l %s" % bvecsFN)
if len(se) > 0 or len(so) == 0:
error_log("Cannot perform wc on bvecs file: %s" % bvecsFN, logFN=logFN)
ln = int(so.split(" ")[0])
info_log("ln = %d" % ln, logFN=logFN)
if ln < 3:
error_log("Unrecognized format in bvecs file: %s" % bvecsFN, \
logFN=logFN)
elif ln == 3:
#== Convert bvecs file ==#
bvecs = np.genfromtxt(bvecsFN)
assert(len(bvecs) == ln)
bvecs = bvecs.T
np.savetxt(bvecsFN, bvecs, fmt="%.15f")
bvecs = np.genfromtxt(bvecsFN)
lbv = len(bvecs)
assert(lbv > 3)
info_log("INFO: Swapped rows and columns in bvecs file: %s\n" \
% bvecsFN,
logFN=logFN)
#== Convert bvals file ==#
bvals = np.genfromtxt(bvalsFN).T
np.savetxt(bvalsFN, bvals, fmt="%.15f")
info_log("INFO: Swapped rows and columns in bvecs file: %s\nc" \
% bvecsFN,
logFN=logFN)
def format_bvals_bvecs(bvalsFN, bvecsFN, logFN=None):
from scai_utils import cmd_stdout, info_log, error_log
import numpy as np
#=== Check the format of bvals and bvecs ===#
import numpy as np
(so, se) = cmd_stdout("wc -l %s" % bvecsFN)
if len(se) > 0 or len(so) == 0:
error_log("Cannot perform wc on bvecs file: %s" % bvecsFN, logFN=logFN)
ln = int(so.split(" ")[0])
info_log("ln = %d" % ln, logFN=logFN)
if ln < 3:
error_log("Unrecognized format in bvecs file: %s" % bvecsFN, \
logFN=logFN)
elif ln == 3:
#== Convert bvecs file ==#
bvecs = np.genfromtxt(bvecsFN)
assert (len(bvecs) == ln)
bvecs = bvecs.T
np.savetxt(bvecsFN, bvecs, fmt="%.15f")
bvecs = np.genfromtxt(bvecsFN)
lbv = len(bvecs)
assert (lbv > 3)
info_log("INFO: Swapped rows and columns in bvecs file: %s\n" \
% bvecsFN,
logFN=logFN)
#== Convert bvals file ==#
bvals = np.genfromtxt(bvalsFN).T
np.savetxt(bvalsFN, bvals, fmt="%.15f")
info_log("INFO: Swapped rows and columns in bvecs file: %s\nc" \
% bvecsFN,
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)
savemat(matFile, intRes)
info_log("Saved intermediate results to file: %s" % matFile)
info_log("Loaded intermediate results from file: %s" % matFile)
intRes = loadmat(matFile)
#== Verify the intermediate results ==#
bVerified = True;
bVerified = bVerified and (list(intRes["sIDs"]) == sIDs)
bVerified = bVerified and (list(intRes["grps"]) == grps)
if bVerified:
info_log("Intermediate results verified.")
else:
error_log("Intermediate results veroficiation failed. Use --redo option to generate valid intermediate results.")
morphInfo = intRes["morphInfo"]
uniqueROIs = list(intRes["uniqueROIs"])
uniqueROIs = [x.strip() for x in uniqueROIs]
area_mm2 = intRes["area_mm2"][0]
#=== Test analysis ===#
import matplotlib.pyplot as plt
roi_a = "rh_vPMC"
roi_b = "rh_PT"
ridx_a = uniqueROIs.index(roi_a)
help="Force redo all steps (default: false)")
if len(sys.argv) == 1:
ap.print_help()
sys.exit(0)
args = ap.parse_args()
#=== Input sanity check ===#
check_dir(args.batchBase)
check_dir(args.fsDir)
#=== Check that SUBJECTS_DIR matches input argument ===#
envFSDir = os.path.abspath(os.getenv("SUBJECTS_DIR"))
if envFSDir != os.path.abspath(args.fsDir):
error_log("Input FreeSurfer SUBJECTS_DIR does not match environmental SUBECTS_DIR")
#=== Get the list of subjects ===#
if args.group == None or len(args.group) == 0:
wc = "*_*"
bAll = True
else:
wc = args.group + "*"
bAll = False;
ds = glob.glob(os.path.join(args.batchBase, wc))
if len(ds) == 0:
raise Exception, "Cannot find subjects that match the selection criterion in directory: %s" % args.batchBase
subjIDs = []
subjIsAWS = []
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)
r_L1Dir = os.path.join(sID, "firstlevel_%s" % machineSettings[hostName]["modelName"])
r_func2Struct_dat = os.path.join(sID, "nii", "func2struct.bbr.dat")
spmTViewCmd_vol = "CONTR=%s; tkmedit ${S} T1.mgz -surfs -overlay ${RHYBASE}/%s/spmT_${CONTR}.img -overlay-reg ${RHYBASE}/%s -fthresh 6 -fmid 9" \
% (strContr, r_L1Dir, r_func2Struct_dat)
info_log("# Commands for viewing spmT for contrast #%d in the volume: " % (i0 + 1))
info_log("\t%s" % spmTViewCmd_vol)
info_log("# Command for viewing spmT for contrast #%d on the surface: " \
% (i0 + 1))
for hemi in HEMIS:
spmTViewCmd_surf = "CONTR=%s; HEMI=%s; tksurfer ${S} ${HEMI} inflated -gray -overlay ${RHYBASE}/%s/spmT_${CONTR}.img -ovelay-reg ${RHYBASE}/%s -fthresh 6 -fmid 9" \
% (strContr, hemi, r_L1Dir, r_func2Struct_dat)
info_log("\t%s" % spmTViewCmd_surf)
info_log(" ")
#=== (Optional): Automatically run the batch commands ===%
if args.runBatch != None and args.runBatch != "":
if args.runBatch.lower() == "all":
for (i0, cmd) in enumerate(batchCmds):
saydo("%s; %s" % (cdCmd, cmd))
else:
if batchSteps.count(args.runBatch) == 0:
error_log("Unrecognized step: %s" % args.runBatch)
else:
saydo("%s; %s" % (cdCmd, batchCmds[batchSteps.index(args.runBatch)]))
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 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 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)
