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)
# functions for connectivity metric from make_local_connectivity_ones import * # name of the maskfile that we will be using roidir = "/home2/data/Projects/CWAS/share/development+motion/rois" maskfile = path.join(roidir, "mask_gray_4mm.nii.gz") ### # 2. Generate Random Connectivity Matrix ### print "2. Random Connectivity Matrix" outfile = path.join(obase, "random_ones_conn.npy") make_local_connectivity_ones(maskfile, outfile) ### # 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
# functions for connectivity metric from make_local_connectivity_ones import * # name of the maskfile that we will be using roidir = "/home2/data/Projects/CWAS/share/age+gender/analysis/03_robustness/rois" maskfile = path.join(roidir, "mask_for_age+sex_gray_4mm.nii.gz") ### # 2. Generate Random Connectivity Matrix ### print "2. Random Connectivity Matrix" outfile = path.join(obase, "random_ones_conn.npy") make_local_connectivity_ones(maskfile, outfile) ### # 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")
# the name of the maskfile that we will be using
maskname = "gm_maskfile.nii.gz"
# make a list of all of the input fMRI files that we will be using
infiles = ['subject1.nii.gz', 'subject2.nii.gz', 'subject3.nii.gz']
##### Step 1. Individual Conenctivity Matrices
# first we need to make the individual connectivity matrices, I will
# do this for all three different kinds (tcorr, scorr, ones) but you
# will only need to do it for one
# the easiest is random clustering which doesn't require any functional
# data, just the mask
print 'ones connectivity'
make_local_connectivity_ones(maskname, 'rm_ones_connectivity.npy')
# construct the connectivity matrices using tcorr and a r>0.5 threshold
for idx, in_file in enumerate(infiles):
# construct an output filename for this file
outname = 'rm_tcorr_conn_' + str(idx) + '.npy'
print 'tcorr connectivity', in_file
# call the funtion to make connectivity
make_local_connectivity_tcorr(in_file, maskname, outname, 0.5)
# construct the connectivity matrices using scorr and a r>0.5 threshold
# This can take a _really_ long time
for idx, in_file in enumerate(infiles):
# the name of the maskfile that we will be using
maskname="gm_maskfile.nii.gz"
# make a list of all of the input fMRI files that we will be using
infiles = [ 'subject1.nii.gz', 'subject2.nii.gz', 'subject3.nii.gz' ]
##### Step 1. Individual Conenctivity Matrices
# first we need to make the individual connectivity matrices, I will
# do this for all three different kinds (tcorr, scorr, ones) but you
# will only need to do it for one
# the easiest is random clustering which doesn't require any functional
# data, just the mask
print 'ones connectivity'
if not os.path.isfile('rm_ones_connectivity.npy'): make_local_connectivity_ones( maskname, 'rm_ones_connectivity.npy')
# construct the connectivity matrices using tcorr and a r>0.5 threshold
for idx, in_file in enumerate(infiles):
# construct an output filename for this file
outname='rm_tcorr_conn_'+str(idx)+'.npy'
print 'tcorr connectivity',in_file
# call the funtion to make connectivity
make_local_connectivity_tcorr( in_file, maskname, outname, 0.5 )
# the name of the maskfile that we will be using
maskname="gm_maskfile.nii.gz"
# make a list of all of the input fMRI files that we will be using
infiles = [ 'subject1.nii.gz', 'subject2.nii.gz', 'subject3.nii.gz' ]
##### Step 1. Individual Conenctivity Matrices
# first we need to make the individual connectivity matrices, I will
# do this for all three different kinds (tcorr, scorr, ones) but you
# will only need to do it for one
# the easiest is random clustering which doesn't require any functional
# data, just the mask
print 'ones connectivity'
make_local_connectivity_ones( maskname, 'rm_ones_connectivity.npy')
# construct the connectivity matrices using tcorr and a r>0.5 threshold
for idx, in_file in enumerate(infiles):
# construct an output filename for this file
outname='rm_tcorr_conn_'+str(idx)+'.npy'
print 'tcorr connectivity',in_file
# call the funtion to make connectivity
make_local_connectivity_tcorr( in_file, maskname, outname, 0.5 )
# construct the connectivity matrices using scorr and a r>0.5 threshold
