def main(maskname, output, k):
"""
This loops through input subjects, constructs individual cortical cluster
maps, and then generates a single map at the group level.
"""
# keep track of the time
T0 = time()
# perform random clustering
localcon = os.path.dirname(maskname) + "/rm_ones_connectivity.npy"
if os.path.isfile(localcon) == False:
# calculates local connectivity from binary mask
make_local_connectivity_ones(maskname, localcon)
if os.path.isfile(output + str(k[0]) + ".npy") == False:
# segments into k clusters using local connectivity
binfile_parcellate(localcon, output, k)
if os.path.isfile(output + str(k[0]) + ".nii.gz") == False:
# write out a nifti
make_image_from_bin_renum(output + str(k[0]) + ".nii.gz", output + str(k[0]) + ".npy", maskname)
T1 = time()
print "**** Done in " + str(T1 - T0)
def main(maskname, output, k):
"""
This loops through input subjects, constructs individual cortical cluster
maps, and then generates a single map at the group level.
"""
# keep track of the time
T0 = time()
# perform random clustering
localcon = os.path.dirname(maskname) + '/rm_ones_connectivity.npy'
if os.path.isfile(localcon) == False:
# calculates local connectivity from binary mask
make_local_connectivity_ones(maskname, localcon)
if os.path.isfile(output + str(k[0]) + '.npy') == False:
# segments into k clusters using local connectivity
binfile_parcellate(localcon, output, k)
if os.path.isfile(output + str(k[0]) + '.nii.gz') == False:
# write out a nifti
make_image_from_bin_renum(output + str(k[0]) + '.nii.gz',
output + str(k[0]) + '.npy', maskname)
T1 = time()
print '**** Done in ' + str(T1 - T0)
###
print "3. Clustering"
from binfile_parcellation import *
ks = [25,50,100,200,400,800,1600,3200]
# For random custering, this is all we need to do, there is no need for group
# level clustering, remember that the output filename is a prefix, and
infile = outfile
outbase = path.join(obase, "random_ones_cluster")
binfile_parcellate(infile, outbase, ks)
###
# 4. Save
###
print "4. Save"
from make_image_from_bin_renum import *
for k in ks:
print "\tk #%i" % k
binfile = path.join(obase, "random_ones_cluster_%i.npy" % k)
imgfile = path.join(roidir, "rois_random_k%04i.nii.gz" % k)
make_image_from_bin_renum(imgfile, binfile, maskfile)
###
# 3. Group-Mean Clustering
###
# Done in 05*
###
# 4. Convert binary output .npy files to nifti
###
ks = [5,10,20,25,40,50,80,100,150,160,200,250,300,320,350,400,450,500,
550,600,640]
network_names = ["visual", "somatomotor", "dorsal_attention",
"ventral_attention", "limbic", "frontoparietal",
"default"]
for network in network_names:
print "Network: %s" % network
for k in ks:
print "\tk #%i" % k
binfile = path.join(obase,
"group_mean_scorr_cluster_%s_%i.npy" % (network, k))
imgfile = path.join(obase,
"group_mean_scorr_cluster_%s_%i.nii.gz" % (network, k))
roifile = path.join(rbase,
"yeo_%s_3mm.nii.gz" % network)
make_image_from_bin_renum(imgfile, binfile, roifile)
###
# 3. 'Clustering'
###
print "3. Clustering"
from binfile_parcellation import *
ks = [25, 50, 100, 200, 400, 800, 1600, 3200]
# For random custering, this is all we need to do, there is no need for group
# level clustering, remember that the output filename is a prefix, and
infile = outfile
outbase = path.join(obase, "random_ones_cluster")
binfile_parcellate(infile, outbase, ks)
###
# 4. Save
###
print "4. Save"
from make_image_from_bin_renum import *
for k in ks:
print "\tk #%i" % k
binfile = path.join(obase, "random_ones_cluster_%i.npy" % k)
imgfile = path.join(roidir, "rois_random_k%04i.nii.gz" % k)
make_image_from_bin_renum(imgfile, binfile, maskfile)
###
# 3. Group-Mean Clustering
###
# Done in 05*
###
# 4. Convert binary output .npy files to nifti
###
ks = [
5, 10, 20, 25, 40, 50, 80, 100, 150, 160, 200, 250, 300, 320, 350, 400,
450, 500, 550, 600, 640
]
network_names = [
"visual", "somatomotor", "dorsal_attention", "ventral_attention", "limbic",
"frontoparietal", "default"
]
for network in network_names:
print "Network: %s" % network
for k in ks:
print "\tk #%i" % k
binfile = path.join(
obase, "group_mean_scorr_cluster_%s_%i.npy" % (network, k))
imgfile = path.join(
obase, "group_mean_scorr_cluster_%s_%i.nii.gz" % (network, k))
roifile = path.join(rbase, "yeo_%s_3mm.nii.gz" % network)
make_image_from_bin_renum(imgfile, binfile, roifile)
