def test_infomap_partition():
""" Test informap partition function """
tmpmat = np.ones((10, 10))
util.fill_diagonal(tmpmat, 0)
graph = nx.from_numpy_matrix(tmpmat, nx.Graph(weighted=False))
part = mod.infomap_partition(graph)
## if all edges are connected expect only one partition
expected_part = {0: set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])}
nt.assert_equal(part.index, expected_part)
def test_newman_partition():
""" Test Newman Partition function """
tmpmat = np.random.random((10, 10))
tmpmat[tmpmat < .5] = 0
graph = nx.from_numpy_matrix(tmpmat, nx.Graph(weighted=False))
npt.assert_raises(ValueError, mod.newman_partition, graph)
tmpmat[:] = 0
# test that no edges raises error (from GraphPartition)
graph = nx.from_numpy_matrix(tmpmat, nx.Graph(weighted=False))
npt.assert_raises(ValueError, mod.newman_partition, graph)
tmpmat[:] = 1
util.fill_diagonal(tmpmat, 0)
graph = nx.from_numpy_matrix(tmpmat, nx.Graph(weighted=False))
part = mod.newman_partition(graph)
## if all edges are connected expect only one partition
expected_part = {0: set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])}
nt.assert_equal(part.index, expected_part)
def test_newman_partition():
""" Test Newman Partition function """
tmpmat = np.random.random((10, 10))
tmpmat[tmpmat < 0.5] = 0
graph = nx.from_numpy_matrix(tmpmat, nx.Graph(weighted=False))
npt.assert_raises(ValueError, mod.newman_partition, graph)
tmpmat[:] = 0
# test that no edges raises error (from GraphPartition)
graph = nx.from_numpy_matrix(tmpmat, nx.Graph(weighted=False))
npt.assert_raises(ValueError, mod.newman_partition, graph)
tmpmat[:] = 1
util.fill_diagonal(tmpmat, 0)
graph = nx.from_numpy_matrix(tmpmat, nx.Graph(weighted=False))
part = mod.newman_partition(graph)
## if all edges are connected expect only one partition
expected_part = {0: set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])}
nt.assert_equal(part.index, expected_part)
size = 11
th2 = 0.6
th1 = -th2
# Split the node list in half, and use two colors for each group
split = size / 2
nodes = range(size)
head, tail = nodes[:split], nodes[split:]
labels = ['%s%s' % (chr(i), chr(i + 32)) for i in range(65, 65 + size)]
#labels = map(str,nodes)
colors = ['y' for _ in head] + ['r' for _ in tail]
mat = util.symm_rand_arr(size)
mat = 2 * mat - 1 # map values to [-1,1] range
util.fill_diagonal(mat, 0) # diag==0 so we don't get self-links
layout = nx.circular_layout
G = util.mat2graph(mat, threshold=th1, threshold2=th2)
pfig = nxplot.draw_graph(
G,
labels=labels,
node_colors=colors,
layout=layout,
title=layout.func_name,
#edge_cmap=cm.PuOr
edge_cmap=cm.RdBu,
#edge_cmap=cm.jet,
colorbar=True,
size = 11
th2 = 0.6
th1 = -th2
# Split the node list in half, and use two colors for each group
split = size/2
nodes = range(size)
head,tail = nodes[:split],nodes[split:]
labels = ['%s%s' % (chr(i),chr(i+32)) for i in range(65,65+size)]
#labels = map(str,nodes)
colors = ['y' for _ in head] + ['r' for _ in tail]
mat = util.symm_rand_arr(size)
mat = 2*mat-1 # map values to [-1,1] range
util.fill_diagonal(mat,0) # diag==0 so we don't get self-links
layout = nx.circular_layout
G = util.mat2graph(mat, threshold=th1,threshold2=th2)
pfig = nxplot.draw_graph(G,
labels=labels,
node_colors=colors,
layout = layout,
title = layout.func_name,
#edge_cmap=cm.PuOr
edge_cmap=cm.RdBu,
#edge_cmap=cm.jet,
colorbar=True,
)
