def _run_interface(self, runtime):
fname = self.inputs.volume
#load data, read lines 8~penultimate
datafile = open(fname, 'rb')
data = [i.strip().split() for i in datafile.readlines()]
stringmatrix = data[8:-1]
datafile.close()
if self.inputs.hemi == 'lh': chosenvertices = lhvertices
if self.inputs.hemi == 'rh': chosenvertices = rhvertices
corrmatrix = np.zeros((len(chosenvertices),len(chosenvertices)))
for x, vertex in enumerate(chosenvertices):
for i in xrange(len(chosenvertices)):
corrmatrix[x][i] = abs(float(stringmatrix[vertex][i]))
if self.inputs.cluster_type == 'spectral':
labels = spectral(corrmatrix, n_clusters=self.inputs.n_clusters, mode='arpack')
if self.inputs.cluster_type == 'hiercluster':
labels = Ward(n_clusters=self.inputs.n_clusters).fit_predict(corrmatrix)
if self.inputs.cluster_type == 'kmeans':
labels = km(n_clusters=self.inputs.n_clusters).fit_predict(corrmatrix)
if self.inputs.cluster_type == 'dbscan':
labels = DBSCAN(eps=np.average(corrmatrix)+np.std(corrmatrix)).fit_predict(corrmatrix)
sxfmout = self.inputs.sxfmout
img = nb.load(sxfmout)
outarray = -np.ones(shape=img.shape[0])
for j, cluster in enumerate(labels):
outarray[chosenvertices[j]] = cluster+1
new_img = nb.Nifti1Image(outarray, img.get_affine(), img.get_header())
_, base, _ = split_filename(fname)
nb.save(new_img, os.path.abspath(base + '_clustered.nii'))
return runtime
def _run_interface(self, runtime):
#load data
data = nb.load(self.inputs.in_File).get_data()
corrmatrix = np.squeeze(data)
if self.inputs.cluster_type == 'spectral':
positivecorrs = np.where(
corrmatrix > 0, corrmatrix,
0) #threshold at 0 (spectral uses non-negative values)
newmatrix = np.asarray(
positivecorrs,
dtype=np.double) #spectral expects dtype=double values
labels = spectral(newmatrix,
n_clusters=self.inputs.n_clusters,
eigen_solver='arpack',
assign_labels='discretize')
if self.inputs.cluster_type == 'hiercluster':
labels = Ward(
n_clusters=self.inputs.n_clusters).fit_predict(corrmatrix)
if self.inputs.cluster_type == 'kmeans':
labels = km(
n_clusters=self.inputs.n_clusters).fit_predict(corrmatrix)
if self.inputs.cluster_type == 'dbscan':
labels = DBSCAN(eps=self.inputs.epsilon).fit_predict(corrmatrix)
new_img = nb.Nifti1Image(labels + 1,
None) #+1 because cluster labels start at 0
_, base, _ = split_filename(self.inputs.in_File)
nb.save(
new_img,
os.path.abspath(base + '_' + str(self.inputs.n_clusters) + '_' +
self.inputs.cluster_type + '_' + self.inputs.hemi +
'.nii'))
return runtime
def _run_interface(self, runtime):
#load data
data = nb.load(self.inputs.in_File).get_data()
corrmatrix = np.squeeze(data)
if self.inputs.cluster_type == 'spectral':
positivecorrs = np.where(corrmatrix>0,corrmatrix,0) #threshold at 0 (spectral uses non-negative values)
newmatrix = np.asarray(positivecorrs,dtype=np.double) #spectral expects dtype=double values
labels = spectral(newmatrix, n_clusters=self.inputs.n_clusters, eigen_solver='arpack', assign_labels='discretize')
if self.inputs.cluster_type == 'hiercluster':
labels = Ward(n_clusters=self.inputs.n_clusters).fit_predict(corrmatrix)
if self.inputs.cluster_type == 'kmeans':
labels = km(n_clusters=self.inputs.n_clusters).fit_predict(corrmatrix)
if self.inputs.cluster_type == 'dbscan':
labels = DBSCAN(eps=self.inputs.epsilon).fit_predict(corrmatrix)
new_img = nb.Nifti1Image(labels+1, None) #+1 because cluster labels start at 0
_, base, _ = split_filename(self.inputs.in_File)
nb.save(new_img, os.path.abspath(base+'_'+str(self.inputs.n_clusters)+'_'+self.inputs.cluster_type+'_'+self.inputs.hemi+'.nii'))
return runtime
