def complete(self, add_centroids=True, graph_attrs={}, atlas={}):
super(FiberGraph, self).complete()
assert atlas, "One Atlas must exist for any small graph!"
# since only 1 atlas
atlas_name = atlas.keys()[0]
atlas_regions = atlas[atlas.keys()[0]]
print "Attempting to add atlas labels ..."
if atlas_regions is not None:
f_regions = open(atlas_regions, "rb")
self.graph.vs["region_name"] = f_regions.read().splitlines()
if add_centroids:
print "Adding centroids ..."
cent_loc = os.path.join(os.environ['M2G_HOME'], "data", "Centroids", "centroids.mat")
if not os.path.exists(cent_loc):
get_files()
cent_mat = sio.loadmat(cent_loc)["centroids"]
centroids = []
for row in cent_mat:
centroids.append(str(list(row)))
self.graph.vs["centroid"] = centroids
for key in graph_attrs.keys():
self.graph[key] = graph_attrs[key]
def complete(self, add_centroids=True, graph_attrs={}, atlas={}):
super(FiberGraph, self).complete()
assert atlas, "One Atlas must exist for any small graph!"
# since only 1 atlas
atlas_name = atlas.keys()[0]
atlas_regions = atlas[atlas.keys()[0]]
print "Attempting to add atlas labels ..."
if atlas_regions is not None:
f_regions = open(atlas_regions, "rb")
self.graph.vs["region_name"] = f_regions.read().splitlines()
if add_centroids:
print "Adding centroids ..."
cent_loc = os.path.join(os.environ['M2G_HOME'], "data",
"Centroids", "centroids.mat")
if not os.path.exists(cent_loc):
get_files()
cent_mat = sio.loadmat(cent_loc)["centroids"]
centroids = []
for row in cent_mat:
centroids.append(str(list(row)))
self.graph.vs["centroid"] = centroids
for key in graph_attrs.keys():
self.graph[key] = graph_attrs[key]
def create(roifn=os.path.join(os.environ["M2G_HOME"], "data", "Atlas",
"MNI152_T1_1mm_brain.nii"),
start=2):
"""
Downsamples an atlas from a template brain.
Create a new atlas given some scaling factor determined by the start index. Can be useful if looking for parcellation of certain scale for graph generation.
**Positional Arguments**
roifn: [.nii; nifti image] (default = MNI152)
- Nifti roi mask file name
start: [int] (default = 2)
- The x,y,z start position which determines the scaling.
**Returns**
atlas: [.nii; nifti image]
- Atlas labels in MNI space.
"""
start_time = time()
atlmap = None
print "Loading rois as base ..."
if not os.path.exists(roifn):
get_files()
img = nib.load(roifn)
base = img.get_data()
aff = img.get_affine()
fm = img.file_map
true_dim = base.shape
# Labelling new
label_used = False
print "Labeling new ..."
region_num = 1
step = 1 + (start * 2)
mstart = -start
mend = start + 1
# Align new to scale factor
xdim, ydim, zdim = map(ceil, np.array(base.shape) / float(step))
if step == 1:
assert xdim == base.shape[0] and ydim == base.shape[
1] and zdim == base.shape[2]
resized_base = np.zeros((xdim * step, ydim * step, zdim * step), dtype=int)
resized_base[:base.shape[0], :base.shape[1], :base.shape[2]] = base
base = resized_base
del resized_base
# Create new matrix
new = np.zeros_like(base,
dtype=np.int) # poke my finger in the eye of bjarne
# TODO: Cythonize
for z in xrange(start, base.shape[2] - start, step):
for y in xrange(start, base.shape[1] - start, step):
for x in xrange(start, base.shape[0] - start, step):
if label_used:
region_num += 1 # only increase counter when a label was used
label_used = False
# set other (step*step)-1 around me to same region
for zz in xrange(mstart, mend):
for yy in xrange(mstart, mend):
for xx in xrange(mstart, mend):
if (base[x + xx, y + yy, z + zz]): # Masking
label_used = True
new[x + xx, y + yy, z + zz] = region_num
new = new[:true_dim[0], :true_dim[1], :
true_dim[2]] # shrink new to correct size
print "Your atlas has %d regions ..." % len(np.unique(new))
img = nib.Nifti1Image(new,
affine=img.get_affine(),
header=img.get_header(),
file_map=img.file_map)
del new
print "Building atlas took %.3f sec ..." % (time() - start_time)
return img
def create(roifn=os.path.join(os.environ["M2G_HOME"],"data","Atlas",
"MNI152_T1_1mm_brain.nii"), start=2):
"""
Downsamples an atlas from a template brain.
Create a new atlas given some scaling factor determined by the start index. Can be useful if looking for parcellation of certain scale for graph generation.
**Positional Arguments**
roifn: [.nii; nifti image] (default = MNI152)
- Nifti roi mask file name
start: [int] (default = 2)
- The x,y,z start position which determines the scaling.
**Returns**
atlas: [.nii; nifti image]
- Atlas labels in MNI space.
"""
start_time = time()
atlmap = None
print "Loading rois as base ..."
if not os.path.exists(roifn):
get_files()
img = nib.load(roifn)
base = img.get_data()
aff = img.get_affine()
fm = img.file_map
true_dim = base.shape
# Labelling new
label_used = False
print "Labeling new ..."
region_num = 1
step = 1+(start*2)
mstart = -start
mend = start+1
# Align new to scale factor
xdim, ydim, zdim = map(ceil, np.array(base.shape)/float(step))
if step == 1:
assert xdim == base.shape[0] and ydim == base.shape[1] and zdim == base.shape[2]
resized_base = np.zeros((xdim*step, ydim*step, zdim*step), dtype=int)
resized_base[:base.shape[0], :base.shape[1], :base.shape[2]] = base
base = resized_base
del resized_base
# Create new matrix
new = np.zeros_like(base, dtype=np.int) # poke my finger in the eye of bjarne
# TODO: Cythonize
for z in xrange(start, base.shape[2]-start, step):
for y in xrange(start, base.shape[1]-start, step):
for x in xrange(start, base.shape[0]-start, step):
if label_used:
region_num += 1 # only increase counter when a label was used
label_used = False
# set other (step*step)-1 around me to same region
for zz in xrange(mstart,mend):
for yy in xrange(mstart,mend):
for xx in xrange(mstart,mend):
if (base[x+xx,y+yy,z+zz]): # Masking
label_used = True
new[x+xx,y+yy,z+zz] = region_num
new = new[:true_dim[0], :true_dim[1], :true_dim[2]] # shrink new to correct size
print "Your atlas has %d regions ..." % len(np.unique(new))
img = nib.Nifti1Image(new, affine=img.get_affine(), header=img.get_header(), file_map=img.file_map)
del new
print "Building atlas took %.3f sec ..." % (time()-start_time)
return img
