Merge pull request #1 from krsna6/master

handled change in dimensions between nifti and nibabel packages

README

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+#Changes were made in the lines of "msk=csc_matrx(......)" of both make_local_connectivity_scorr.py & make_local_connectivity_tcorr.py to compensate for the change in dimension order from nifti to nibabel

__init__.py

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+#@krishna Somandepalli

scorr_test.py

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+#### pyClusterROI_test.py
+# Copyright (C) 2010 R. Cameron Craddock ([email protected])
+#
+# This script is a part of the pyClusterROI python toolbox for the spatially
+# constrained clustering of fMRI data. It is a demonstration of how to use the
+# toolbox and a regression test to make sure that the toolbox code works.
+#
+# For more information refer to:
+#
+# Craddock, R. C.; James, G. A.; Holtzheimer, P. E.; Hu, X. P. & Mayberg, H. S.
+# A whole brain fMRI atlas generated via spatially constrained spectral
+# clustering Human Brain Mapping, 2012, 33, 1914-1928 doi: 10.1002/hbm.21333.
+#
+# ARTICLE{Craddock2012,
+#   author = {Craddock, R C and James, G A and Holtzheimer, P E and Hu, X P and
+#   Mayberg, H S},
+#   title = {{A whole brain fMRI atlas generated via spatially constrained
+#   spectral clustering}},
+#   journal = {Human Brain Mapping},
+#   year = {2012},
+#   volume = {33},
+#   pages = {1914--1928},
+#   number = {8},
+#   address = {Department of Neuroscience, Baylor College of Medicine, Houston,
+#       TX, United States},
+#   pmid = {21769991},
+# } 
+#
+# Documentation, updated source code and other information can be found at the
+# NITRC web page: http://www.nitrc.org/projects/cluster_roi/ and on github at
+# https://github.com/ccraddock/cluster_roi
+#
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+####
+
+
+# this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and
+# NiBabel (http://niftilib.sourceforge.net/pynifti/) and the pyClusterROI
+# toolbox to be installed in a directory that is accessible through PythonPath
+# import sys
+
+# this is how you would add a directory to the search path, this is useful if
+# you are running this script from a directory other than the directory where
+# the pyClusterROI is installed. Or if for some reason your NumPy, SciPy, or
+# NiBabel libraries are in a non-standard location, do this before you import
+# the files/libraries that require the change in path
+# sys.path.append("/home/user/python_toolboxes")
+
+# import the different functions we will use from pyClusterROI
+
+# only need one of these, based on which connectivity metric you prefer
+from make_local_connectivity_ones import *
+from make_local_connectivity_scorr import *
+from make_local_connectivity_tcorr import *
+
+# do not need this if you are peforming group mean clustering
+from binfile_parcellation import *
+
+# import the functions for group clustering, only need one of these
+from group_binfile_parcellation import *
+from group_mean_binfile_parcellation import *
+
+# import if you want to write the results out to nifti, only need
+# one of these, probably just want the one that does renumbering,
+# why do i include the other one? no idea.
+from make_image_from_bin import *
+from make_image_from_bin_renum import *
+
+import os
+from time import time
+import nibabel as nb
+
+T0 = time()
+print 'starting at ', T0
+
+# 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 scorr and a r>0.5 threshold
+# This can take a _really_ long time
+for idx, in_file in enumerate(infiles):
+
+    # construct an output filename for this file
+    outname='rm_scorr_conn_'+str(idx)+'.npy'
+
+    print 'scorr connectivity',in_file
+    # call the funtion to make connectivity
+    make_local_connectivity_scorr( in_file, maskname, outname, 0.5 )
+
+##### Step 2. Individual level clustering
+# next we will do the individual level clustering, this is not performed for 
+# group-mean clustering, remember that for these functions the output name
+# is a prefix that will have K and .npy added to it by the functions. We
+# will perform this for clustering between 100, 150 and 200 clusters
+NUM_CLUSTERS = [100,150,200]
+
+# 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 
+binfile_parcellate('rm_ones_connectivity.npy','rm_ones_cluster',NUM_CLUSTERS)
+
+# for scorr
+for idx, in_file in enumerate(infiles):
+
+    # construct filenames
+    infile='rm_scorr_conn_'+str(idx)+'.npy'
+    outfile='rm_scorr_indiv_cluster_'+str(idx)
+
+    print 'scorr parcellate',in_file
+    binfile_parcellate(infile, outfile, NUM_CLUSTERS)
+
+##### Step 3. Group level clustering
+# perform the group level clustering for clustering results containing 100, 150,
+# and 200 clusters. as previously mentioned, this does _not_ have to be done for
+# random clustering
+
+# for both group-mean and 2-level clustering we need to know the number of
+# voxels in in the mask, which for us is 32254 
+mask_ = nb.load(maskname).get_data()
+mask_voxels=len(mask_[mask_==1])
+print 'NUMBER OF NONZERO VOXELS IN THE MASK = ', mask_voxels
+
+# now group mean cluster scorr files
+scorr_conn_files=['rm_scorr_conn_0.npy','rm_scorr_conn_1.npy',\
+    'rm_scorr_conn_2.npy']
+print 'group-mean parcellate scorr'
+group_mean_binfile_parcellate( scorr_conn_files,\
+    'rm_group_mean_scorr_cluster', NUM_CLUSTERS, mask_voxels);
+
+# the 2-level clustering has to be performed once for each desired clustering
+# level, and requires individual level clusterings as inputs
+
+# now for scorr 
+for k in NUM_CLUSTERS:
+    ind_clust_files=[]
+    for i in range(0,len(infiles)):
+	ind_clust_files.append('rm_scorr_indiv_cluster_'+str(i)+\
+	    '_'+str(k)+'.npy')
+
+    print '2-level parcellate scorr',k
+    group_binfile_parcellate(ind_clust_files,\
+	'rm_group_scorr_cluster_'+str(k)+'.npy',k,mask_voxels)
+
+##### Step 4. Convert the binary output .npy files to nifti
+# this can be done with or without renumbering the clusters to make sure they
+# are contiguous. remember, we might end up with fewer clusters than we ask for,
+# and this could result in gaps in the cluster numbering. Choose which you like,
+# i use them intermittently below as a regression test
+
+# write out for the random clustering
+
+for k in NUM_CLUSTERS:
+    binfile='rm_ones_cluster_'+str(k)+'.npy'
+    imgfile='rm_ones_cluster_'+str(k)+'.nii.gz'
+    make_image_from_bin(imgfile,binfile,maskname);
+
+
+for k in NUM_CLUSTERS:
+    binfile='rm_group_mean_scorr_cluster_'+str(k)+'.npy'
+    imgfile='rm_group_mean_scorr_cluster_'+str(k)+'.nii.gz'
+    make_image_from_bin_renum(imgfile,binfile,maskname)
+
+
+for k in NUM_CLUSTERS:
+    binfile='rm_group_scorr_cluster_'+str(k)+'.npy'
+    imgfile='rm_group_scorr_cluster_'+str(k)+'.nii.gz'
+    make_image_from_bin_renum(imgfile,binfile,maskname)
+
+T1 = time()
+
+print '******************************'
+print 'time taken to complete scorr based spatially constrained clustering is ', T1-T0
+##### FIN
+