# HG changeset patch # User Sean Farley # Date 1354652310 21600 # Node ID b29f471a1754450f07859ca0f6e0f7b5ba1942fb # Parent 5687b9076dd8350facc03263810765de6079924b rename: nifti -> nibabel diff --git a/make_image_from_bin.py b/make_image_from_bin.py --- a/make_image_from_bin.py +++ b/make_image_from_bin.py @@ -1,11 +1,11 @@ #### make_image_from_bin.py # Copyright (C) 2010 R. Cameron Craddock (cameron.craddock@gmail.com) # # This script is a part of the pyClusterROI python toolbox for the spatially constrained clustering of fMRI # data. It converts a NPY file generated by binfile_parcellation.py, group_binfile_parcellation.py, or -# group_mean_binfile_parcellation.npy into a nifti file where each voxels intensity corresponds to the +# group_mean_binfile_parcellation.npy into a nibabel file where each voxels intensity corresponds to the # number of the cluster to which it belongs. # # For more information refer to: # # Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2011). A whole @@ -29,24 +29,24 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . #### # this scripts requires NumPy (numpy.scipy.org), and PyNIfTI -# (http://niftilib.sourceforge.net/pynifti/) to be installed in a directory that is +# (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nifti import NiftiImage +from nibabel import NiftiImage from numpy import * # make_image_from_bin( image, binfile, mask ) # # Converts a NPY file generated by binfile_parcellation.py, group_binfile_parcellation.py, or -# group_mean_binfile_parcellation.npy into a nifti file where each voxels intensity corresponds to the +# group_mean_binfile_parcellation.npy into a nibabel file where each voxels intensity corresponds to the # number of the cluster to which it belongs. -# image: The name of the nifti file to be written. +# image: The name of the nibabel file to be written. # binfile: The binfile to be converted. The file contains a n_voxel x 1 vector that is -# converted to a nifti file. -# mask: Mask describing the space of the nifti file. This should correspond to the +# converted to a nibabel file. +# mask: Mask describing the space of the nibabel file. This should correspond to the # mask originally used to create the connectivity matrices used for parcellation. # def make_image_from_bin( image, binfile, mask ): # read in the mask @@ -68,11 +68,11 @@ print "sum",sum(imdat) # map the binary data to mask print "shape2",shape(a[0:sum(imdat)]) imdat[imdat>0]=short(a[0:sum(imdat)].flatten()) - # write out the image as nifti + # write out the image as nibabel thdr=nim.header thdr['scl_slope']=1 nim_out=NiftiImage(imdat) nim_out.updateFromDict(thdr) diff --git a/make_image_from_bin_renum.py b/make_image_from_bin_renum.py --- a/make_image_from_bin_renum.py +++ b/make_image_from_bin_renum.py @@ -1,11 +1,11 @@ #### make_image_from_bin_renum.py # Copyright (C) 2010 R. Cameron Craddock (cameron.craddock@gmail.com) # # This script is a part of the pyClusterROI python toolbox for the spatially constrained clustering of fMRI # data. It converts a NPY file generated by binfile_parcellation.py, group_binfile_parcellation.py, or -# group_mean_binfile_parcellation.npy into a nifti file where each voxels intensity corresponds to the +# group_mean_binfile_parcellation.npy into a nibabel file where each voxels intensity corresponds to the # number of the cluster to which it belongs. The clusters are renumbered to be contiguous. # # For more information refer to: # # Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2011). A whole @@ -29,24 +29,24 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . #### # this scripts requires NumPy (numpy.scipy.org), and PyNIfTI -# (http://niftilib.sourceforge.net/pynifti/) to be installed in a directory that is +# (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nifti import NiftiImage +from nibabel import NiftiImage from numpy import * # make_image_from_bin_renum( image, binfile, mask ): # # Converts a NPY file generated by binfile_parcellation.py, group_binfile_parcellation.py, or -# group_mean_binfile_parcellation.npy into a nifti file where each voxels intensity corresponds to the +# group_mean_binfile_parcellation.npy into a nibabel file where each voxels intensity corresponds to the # number of the cluster to which it belongs. Clusters are renumberd to be contiguous. -# image: The name of the nifti file to be written. +# image: The name of the nibabel file to be written. # binfile: The binfile to be converted. The file contains a n_voxel x 1 vector that is -# converted to a nifti file. -# mask: Mask describing the space of the nifti file. This should correspond to the +# converted to a nibabel file. +# mask: Mask describing the space of the nibabel file. This should correspond to the # mask originally used to create the connectivity matrices used for parcellation. # def make_image_from_bin_renum( image, binfile, mask ): @@ -76,11 +76,11 @@ # map the binary data to mask imdat[imdat>0]=1 imdat[imdat>0]=short(b[0:sum(imdat)].flatten()) - # write out the image as nifti + # write out the image as nibabel thdr=nim.header thdr['scl_slope']=1 nim_out=NiftiImage(imdat) nim_out.updateFromDict(thdr) diff --git a/make_local_connectivity_ones.py b/make_local_connectivity_ones.py --- a/make_local_connectivity_ones.py +++ b/make_local_connectivity_ones.py @@ -28,13 +28,13 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . #### # this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and PyNIfTI -# (http://niftilib.sourceforge.net/pynifti/) to be installed in a directory that is +# (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nifti import * +from nibabel import * from numpy import array from scipy import * from scipy.sparse import * # simple function to translate 1D vector coordinates to 3D matrix coordinates, for a 3D matrix of diff --git a/make_local_connectivity_scorr.py b/make_local_connectivity_scorr.py --- a/make_local_connectivity_scorr.py +++ b/make_local_connectivity_scorr.py @@ -29,13 +29,13 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . #### # this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and PyNIfTI -# (http://niftilib.sourceforge.net/pynifti/) to be installed in a directory that is +# (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nifti import * +from nibabel import * from numpy import array from scipy import * from scipy.sparse import * from scipy.sparse.linalg.eigen.arpack import eigsh from scipy.linalg import norm, svd diff --git a/make_local_connectivity_tcorr.py b/make_local_connectivity_tcorr.py --- a/make_local_connectivity_tcorr.py +++ b/make_local_connectivity_tcorr.py @@ -29,13 +29,13 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . #### # this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and PyNIfTI -# (http://niftilib.sourceforge.net/pynifti/) to be installed in a directory that is +# (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nifti import * +from nibabel import * from numpy import array from scipy import * from scipy.sparse import * from scipy.sparse.linalg.eigen.arpack import eigsh from scipy.linalg import norm, svd diff --git a/pyClusterROI_test.py b/pyClusterROI_test.py --- a/pyClusterROI_test.py +++ b/pyClusterROI_test.py @@ -28,11 +28,11 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . #### # this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and PyNIfTI -# (http://niftilib.sourceforge.net/pynifti/) and the pyClusterROI toolbox to be +# (http://nibabellib.sourceforge.net/pynibabel/) 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 @@ -53,11 +53,11 @@ # 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 +# import if you want to write the results out to nibabel, 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 * @@ -168,11 +168,11 @@ 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 +##### Step 4. Convert the binary output .npy files to nibabel # 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 # HG changeset patch # User Sean Farley # Date 1354653834 21600 # Node ID fc76d109f560fa5790eb268b6f4f81d78c68b2a3 # Parent b29f471a1754450f07859ca0f6e0f7b5ba1942fb nifti: update to use newer nibabel diff --git a/make_image_from_bin.py b/make_image_from_bin.py --- a/make_image_from_bin.py +++ b/make_image_from_bin.py @@ -31,11 +31,11 @@ #### # this scripts requires NumPy (numpy.scipy.org), and PyNIfTI # (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nibabel import NiftiImage +from nibabel.nifti1 import load as NiftiImage from numpy import * # make_image_from_bin( image, binfile, mask ) # # Converts a NPY file generated by binfile_parcellation.py, group_binfile_parcellation.py, or diff --git a/make_image_from_bin_renum.py b/make_image_from_bin_renum.py --- a/make_image_from_bin_renum.py +++ b/make_image_from_bin_renum.py @@ -31,11 +31,11 @@ #### # this scripts requires NumPy (numpy.scipy.org), and PyNIfTI # (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nibabel import NiftiImage +from nibabel.nifti1 import load as NiftiImage from numpy import * # make_image_from_bin_renum( image, binfile, mask ): # # Converts a NPY file generated by binfile_parcellation.py, group_binfile_parcellation.py, or diff --git a/make_local_connectivity_ones.py b/make_local_connectivity_ones.py --- a/make_local_connectivity_ones.py +++ b/make_local_connectivity_ones.py @@ -30,11 +30,11 @@ #### # this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and PyNIfTI # (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nibabel import * +from nibabel.nifti1 import load as NiftiImage from numpy import array from scipy import * from scipy.sparse import * # simple function to translate 1D vector coordinates to 3D matrix coordinates, for a 3D matrix of @@ -81,14 +81,14 @@ [-1, 1, 1],[0, 1, 1],[1, 1, 1]]) # read in the mask msk=NiftiImage(maskfile) - msz=shape(msk.data) + msz=shape(msk.get_data()) # convert the 3D mask array into a 1D vector - mskdat=reshape(msk.data,prod(msz)) + mskdat=reshape(msk.get_data(),prod(msz)) # determine the 1D coordinates of the non-zero # elements of the mask iv=nonzero(mskdat)[0] m=len(iv) diff --git a/make_local_connectivity_scorr.py b/make_local_connectivity_scorr.py --- a/make_local_connectivity_scorr.py +++ b/make_local_connectivity_scorr.py @@ -31,11 +31,11 @@ #### # this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and PyNIfTI # (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nibabel import * +from nibabel.nifti1 import load as NiftiImage from numpy import array from scipy import * from scipy.sparse import * from scipy.sparse.linalg.eigen.arpack import eigsh from scipy.linalg import norm, svd @@ -90,26 +90,26 @@ [-1, 1, 1],[0, 1, 1],[1, 1, 1]]) # read in the mask msk=NiftiImage(maskfile) - msz=shape(msk.data) + msz=shape(msk.get_data()) # convert the 3D mask array into a 1D vector - mskdat=reshape(msk.data,prod(msz)) + mskdat=reshape(msk.get_data(),prod(msz)) # determine the 1D coordinates of the non-zero # elements of the mask iv=nonzero(mskdat)[0] m=len(iv) # read in the fmri data nim=NiftiImage(infile) - sz=shape(nim.data) + sz=shape(nim.get_data()) # reshape fmri data to a num_voxels x num_timepoints array - imdat=reshape(nim.data,(sz[0],prod(sz[1:]))) + imdat=reshape(nim.get_data(),(sz[0],prod(sz[1:]))) # mask the datset to only then in-mask voxels imdat=imdat[:,iv] #zscore fmri time courses, this makes calculation of the diff --git a/make_local_connectivity_tcorr.py b/make_local_connectivity_tcorr.py --- a/make_local_connectivity_tcorr.py +++ b/make_local_connectivity_tcorr.py @@ -31,11 +31,11 @@ #### # this scripts requires NumPy (numpy.scipy.org), SciPy (www.scipy.org), and PyNIfTI # (http://nibabellib.sourceforge.net/pynibabel/) to be installed in a directory that is # accessible through PythonPath -from nibabel import * +from nibabel.nifti1 import load as NiftiImage from numpy import array from scipy import * from scipy.sparse import * from scipy.sparse.linalg.eigen.arpack import eigsh from scipy.linalg import norm, svd @@ -91,26 +91,26 @@ [-1, 1, 1],[0, 1, 1],[1, 1, 1]]) # read in the mask msk=NiftiImage(maskfile) - msz=shape(msk.data) + msz=shape(msk.get_data()) # convert the 3D mask array into a 1D vector - mskdat=reshape(msk.data,prod(msz)) + mskdat=reshape(msk.get_data(),prod(msz)) # determine the 1D coordinates of the non-zero # elements of the mask iv=nonzero(mskdat)[0] m=len(iv) # read in the fmri data nim=NiftiImage(infile) - sz=shape(nim.data) + sz=shape(nim.get_data()) # reshape fmri data to a num_voxels x num_timepoints array - imdat=reshape(nim.data,(sz[0],prod(sz[1:]))) + imdat=reshape(nim.get_data(),(sz[0],prod(sz[1:]))) # construct a sparse matrix from the mask msk=csc_matrix((range(1,m+1),(iv,zeros(m))),shape=(prod(sz[1:]),1)) sparse_i=[]