def _forward_dataset(self, ds):
chunks_attr = self.__chunks_attr
mds = Dataset([])
mds.a = ds.a
# mds.sa =ds.sa
# mds.fa =ds.fa
if chunks_attr is None:
# global kmeans
mds.samples = self._kmeans(ds.samples).labels_
print max(mds.samples)
else:
# per chunk kmeans
for c in ds.sa[chunks_attr].unique:
slicer = np.where(ds.sa[chunks_attr].value == c)[0]
mds.samples = ds.samples[0,:]
mds.samples[slicer] = self._kmeans(ds.samples[slicer]).labels_
return mds
def _forward_dataset(self, ds):
chunks_attr = self.__chunks_attr
mds = Dataset([])
mds.a = ds.a
# mds.sa =ds.sa
# mds.fa =ds.fa
if chunks_attr is None:
# global kmeans
mds.samples = self._kmeans(ds.samples).labels_
print max(mds.samples)
else:
# per chunk kmeans
for c in ds.sa[chunks_attr].unique:
slicer = np.where(ds.sa[chunks_attr].value == c)[0]
mds.samples = ds.samples[0, :]
mds.samples[slicer] = self._kmeans(ds.samples[slicer]).labels_
return mds
def _forward_dataset(self, ds):
out_ds = Dataset([])
out_ds.a = ds.a
pdb.set_trace()
iv = np.nonzero(ds.samples)[0]
coords = ds.sa.values()[0][iv]
out_ds.fa = coords
dim = ds.a.voxel_dim
nbdim = self.__neighbor_shape.nbdim
nbsize = self.__neighbor_shape.nbsize
shape_type = self.__neighbor_shape.shape_type
volnb = volneighbors(coords, dim, nbdim, nbsize, shape_type)
distmsk = volnb.compute_offsets()
if self.__outsparse == True:
out_ds.samples = distmask
elif self.__outsparse == False:
distmask = distmask.todense()
out_ds.samples = distmask
else:
raise RuntimeError('%outsparse should be True or False.')
return out_ds
def _call(self, ds):
'''
Parameters
----------
ds: Dataset
input dataset
Returns
-------
wds: Dataset
Result with one sample (if axis=='feature') or one feature (if
axis=='samples') and an equal number of features (or samples,
respectively) as the input dataset.
'''
axis = self.__axis
fx = self.__fx
# ensure it's a dataset
if not isinstance(ds, Dataset):
ds = Dataset(ds)
samples = ds.samples
# apply the function
winners = fx(ds)
# set the new shape
new_shape = list(ds.shape)
new_shape[axis] = 1
# the output dataset
wta = Dataset(np.reshape(winners, new_shape))
# copy dataset attributes
wta.a = ds.a.copy()
# copy feature attribute and set sample attributes, or vice versa
fas = [ds.fa, wta.fa]
sas = [ds.sa, wta.sa]
fas_sas = [fas, sas]
to_copy, to_leave = [fas_sas[(i + axis) % 2] for i in xrange(2)]
# copy each attribute
for k, v in to_copy[0].iteritems():
to_copy[1][k] = copy.copy(v)
# set source and target. feature attributes become
# sample attribtues; or vice versa
src, _ = to_leave
trg = to_copy[1]
prefix = self.__other_axis_prefix
for k, v in src.iteritems():
# set the prefix
prek = ('' if prefix is None else prefix) + k
if prek in trg:
raise KeyError("Key clash: %s already in %s" %
(prek, to_copy[1]))
trg[prek] = v.value[winners]
return wta
def _call(self, ds):
'''
Parameters
----------
ds: Dataset
input dataset
Returns
-------
wds: Dataset
Result with one sample (if axis=='feature') or one feature (if
axis=='samples') and an equal number of features (or samples,
respectively) as the input dataset.
'''
axis = self.__axis
fx = self.__fx
# ensure it's a dataset
if not isinstance(ds, Dataset):
ds = Dataset(ds)
samples = ds.samples
# apply the function
winners = fx(ds)
# set the new shape
new_shape = list(ds.shape)
new_shape[axis] = 1
# the output dataset
wta = Dataset(np.reshape(winners, new_shape))
# copy dataset attributes
wta.a = ds.a.copy()
# copy feature attribute and set sample attributes, or vice versa
fas = [ds.fa, wta.fa]
sas = [ds.sa, wta.sa]
fas_sas = [fas, sas]
to_copy, to_leave = [fas_sas[(i + axis) % 2] for i in xrange(2)]
# copy each attribute
for k, v in to_copy[0].iteritems():
to_copy[1][k] = copy.copy(v)
# set source and target. feature attributes become
# sample attributes; or vice versa
src, _ = to_leave
trg = to_copy[1]
prefix = self.__other_axis_prefix
for k, v in src.iteritems():
# set the prefix
prek = ('' if prefix is None else prefix) + k
if prek in trg:
raise KeyError("Key clash: %s already in %s"
% (prek, to_copy[1]))
trg[prek] = v.value[winners]
return wta
def _forward_dataset(self, ds):
mds = Dataset([])
mds.a = ds.a
vectordist = self._fdistance(ds.samples)
mds.samples = squareform(vectordist, force='no', checks=True)
return mds
def main():
'''
Spectral clustering...
'''
st = time.time()
tmpset = Dataset([])
# hfilename = "/nfs/j3/userhome/dangxiaobin/workingdir/cutROI/%s/fdt_matrix2_targets_sc.T.hdf5"%(id)
hfilename = 'fdt_matrix2.T.hdf5'
print hfilename
#load connectivity profile of seed mask voxels
conn = open_conn_mat(hfilename)
tmpset.a = conn.a
print conn.shape,conn.a
#remove some features
mask = create_mask(conn.samples,0.5,1)
# print mask,mask.shape
conn_m = mask_feature(conn.samples,mask)
# print conn_m
map = conn_m.T
print "map:"
print map.shape,map.max(),map.min()
voxel = np.array(conn.fa.values())
print voxel[0]
v = voxel[0]
spacedist = ds.cdist(v,v,'euclidean')
print spacedist
"""
similar_mat = create_similarity_mat(map,conn.fa,0.1,2)
X = np.array(similar_mat)
print "similarity matrix: shape:",X.shape
print X
"""
corr = np.corrcoef(map)
corr = np.abs(corr)
corr = 0.1*corr + 0.9/(spacedist+1)
print "Elaspsed time: ", time.time() - st
print corr.shape,corr
plt.imshow(corr,interpolation='nearest',cmap=cm.jet)
cb = plt.colorbar()
pl.xticks(())
pl.yticks(())
pl.show()
cnum = 3
near = 100
sc = SpectralClustering(cnum,'arpack',None,100,1,'precomputed',near,None,True)
#sc.fit(map)
sc.fit_predict(corr)
'''
cnum = 3
near = 100
sc = SpectralClustering(cnum,'arpack',None,100,1,'nearest_neighbors',near,None,True)
sc.fit(map)
# sc.fit_predict(X)
# param = sc.get_params(deep=True)
'''
tmpset.samples = sc.labels_+1
# print sc.affinity_matrix_
#print list(sc.labels_)
print "Elaspsed time: ", time.time() - st
print "Number of voxels: ", sc.labels_.size
print "Number of clusters: ", np.unique(sc.labels_).size
result = map2nifti(tmpset)
result.to_filename("fg_parcel_S0006.nii.gz")
print ".....The end........"
def spectral_seg(hfilename,outf):
'''
Spectral clustering...
'''
tmpset = Dataset([])
#pdb.set_trace()
print "hdf name:",hfilename
st = time.time()
###1.load connectivity profile of seed mask voxels
conn = h5load(hfilename)
tmpset.a = conn.a
print "connection matrix shape:"
print conn.shape
###2.features select
mask = create_mask(conn.samples,5)
conn_m = conn.samples[mask]
map = conn_m.T
print "masked conn matrix:"
print map.shape,map.max(),map.min()
###3.average the connection profile.
temp = np.zeros(map.shape)
voxel = np.array(conn.fa.values())
v = voxel[0]
v = v.tolist()
shape = [256,256,256]
i = 0
for coor in v:
mean_f = map[i]
#print mean_f.shape
#plt.plot(mean_f)
#plt.show()
neigh =get_neighbors(coor,2,shape)
#print "neigh:",neigh
count = 1
for n in neigh:
if n in v:
mean_f = (mean_f*count + map[v.index(n)])/(count+1)
count+=1
temp[i] = mean_f
i+=1
#sys.exit(0)
map = temp
print "average connection matrix"
###4.spacial distance
spacedist = ds.cdist(v,v,'euclidean')
#print spacedist
###5.correlation matrix
corr = np.corrcoef(map)
corr = np.abs(corr)
###6.mix similariry matrix.
corr = 0.7*corr + 0.3/(spacedist+1)
#plt.imshow(corr,interpolation='nearest',cmap=cm.jet)
#cb = plt.colorbar()
#pl.xticks(())
#pl.yticks(())
#pl.show()
print "mix up the corr and spacial matrix"
#sys.exit(0)
###7.spectral segmentation
print "do segmentation"
cnum = 3
near = 100
sc = SpectralClustering(cnum,'arpack',None,100,1,'precomputed',near,None,True)
sc.fit_predict(corr)
tmpset.samples = sc.labels_+1
print "Number of voxels: ", sc.labels_.size
print "Number of clusters: ", np.unique(sc.labels_).size
print "Elapsed time: ", time.time() - st
###8.save the segmentation result.
print "save the result to xxx_parcel.nii.gz"
result = map2nifti(tmpset)
result.to_filename(outf)
print ".....Segment end........"
return True
def main():
'''
Spectral clustering...
'''
st = time.time()
tmpset = Dataset([])
# hfilename = "/nfs/j3/userhome/dangxiaobin/workingdir/cutROI/%s/fdt_matrix2_targets_sc.T.hdf5"%(id)
hfilename = 'fdt_matrix2.T.hdf5'
print hfilename
#load connectivity profile of seed mask voxels
conn = open_conn_mat(hfilename)
tmpset.a = conn.a
print conn.shape, conn.a
#remove some features
mask = create_mask(conn.samples, 0.5, 1)
# print mask,mask.shape
conn_m = mask_feature(conn.samples, mask)
# print conn_m
map = conn_m.T
print "map:"
print map.shape, map.max(), map.min()
voxel = np.array(conn.fa.values())
print voxel[0]
v = voxel[0]
spacedist = ds.cdist(v, v, 'euclidean')
print spacedist
"""
similar_mat = create_similarity_mat(map,conn.fa,0.1,2)
X = np.array(similar_mat)
print "similarity matrix: shape:",X.shape
print X
"""
corr = np.corrcoef(map)
corr = np.abs(corr)
corr = 0.1 * corr + 0.9 / (spacedist + 1)
print "Elaspsed time: ", time.time() - st
print corr.shape, corr
plt.imshow(corr, interpolation='nearest', cmap=cm.jet)
cb = plt.colorbar()
pl.xticks(())
pl.yticks(())
pl.show()
cnum = 3
near = 100
sc = SpectralClustering(cnum, 'arpack', None, 100, 1, 'precomputed', near,
None, True)
#sc.fit(map)
sc.fit_predict(corr)
'''
cnum = 3
near = 100
sc = SpectralClustering(cnum,'arpack',None,100,1,'nearest_neighbors',near,None,True)
sc.fit(map)
# sc.fit_predict(X)
# param = sc.get_params(deep=True)
'''
tmpset.samples = sc.labels_ + 1
# print sc.affinity_matrix_
#print list(sc.labels_)
print "Elaspsed time: ", time.time() - st
print "Number of voxels: ", sc.labels_.size
print "Number of clusters: ", np.unique(sc.labels_).size
result = map2nifti(tmpset)
result.to_filename("fg_parcel_S0006.nii.gz")
print ".....The end........"
def _forward_dataset(self, ds):
mds = Dataset([])
mds.a = ds.a
vectordist = self._fdistance(ds.samples)
mds.samples = squareform(vectordist, force='no', checks=True)
return mds
