def test_connect_regions_with_grid():
lena = sp.lena()
mask = lena > 50
graph = grid_to_graph(*lena.shape, **{'mask': mask})
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
mask = lena > 150
graph = grid_to_graph(*lena.shape, **{'mask': mask, 'dtype': None})
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
def test_connect_regions():
lena = sp.lena()
for thr in (50, 150):
mask = lena > thr
graph = img_to_graph(lena, mask)
nose.tools.assert_equal(
ndimage.label(mask)[1],
cs_graph_components(graph)[0])
def test_grid_to_graph():
#Checking that the function works with graphs containing no edges
size = 2
roi_size = 1
# Generating two convex parts with one vertex
# Thus, edges will be empty in _to_graph
mask = np.zeros((size, size), dtype=np.bool)
mask[0:roi_size, 0:roi_size] = True
mask[-roi_size:, -roi_size:] = True
mask = mask.reshape(size**2)
A = grid_to_graph(n_x=size, n_y=size, mask=mask, return_as=np.ndarray)
# Checking that the function works whatever the type of mask is
mask = np.ones((size, size), dtype=np.int16)
A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask)
assert (cs_graph_components(A)[0] == 1)
def _find_clusters(x, threshold, tail=0, connectivity=None):
"""For a given 1d-array (test statistic), find all clusters which
are above/below a certain threshold. Returns a list of 2-tuples.
Parameters
----------
x: 1D array
Data
threshold: float
Where to threshold the statistic
tail : -1 | 0 | 1
Type of comparison
Returns
-------
clusters: list of slices or list of arrays (boolean masks)
We use slices for 1D signals and mask to multidimensional
arrays.
sums: array
Sum of x values in clusters
"""
if not tail in [-1, 0, 1]:
raise ValueError('invalid tail parameter')
x = np.asanyarray(x)
if tail == -1:
x_in = x < threshold
elif tail == 1:
x_in = x > threshold
else:
x_in = np.abs(x) > threshold
labels, n_labels = ndimage.label(x_in)
if connectivity is None:
if x.ndim == 1:
clusters = ndimage.find_objects(labels, n_labels)
sums = ndimage.measurements.sum(x, labels,
index=range(1, n_labels + 1))
else:
clusters = list()
sums = np.empty(n_labels)
for l in range(1, n_labels + 1):
c = labels == l
clusters.append(c)
sums[l - 1] = np.sum(x[c])
else:
if x.ndim > 1:
raise Exception("Data should be 1D when using a connectivity "
"to define clusters.")
from scikits.learn.utils._csgraph import cs_graph_components
mask = np.logical_and(x_in[connectivity.row], x_in[connectivity.col])
if np.sum(mask) == 0:
return [], np.empty(0)
connectivity = sparse.coo_matrix((connectivity.data[mask],
(connectivity.row[mask],
connectivity.col[mask])))
_, components = cs_graph_components(connectivity)
labels = np.unique(components)
clusters = list()
sums = list()
for l in labels:
c = (components == l)
if np.any(x_in[c]):
clusters.append(c)
sums.append(np.sum(x[c]))
sums = np.array(sums)
return clusters, sums
mask = lena > thr
graph = img_to_graph(lena, mask)
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
def test_connect_regions_with_grid():
lena = sp.lena()
mask = lena > 50
graph = grid_to_graph(*lena.shape, **{'mask': mask})
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
mask = lena > 150
<<<<<<< HEAD
graph = grid_to_graph(*lena.shape, **{'mask' : mask, 'dtype' : None})
nose.tools.assert_equal(ndimage.label(mask)[1],
cs_graph_components(graph)[0])
if __name__ == '__main__':
import nose
nose.runmodule()
=======
graph = grid_to_graph(*lena.shape, **{'mask': mask, 'dtype': None})
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
def _downsampled_lena():
lena = sp.lena()
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + \
lena[1::2, 1::2]
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + \
lena[1::2, 1::2]