def test_output(flag_file):
adjacency_matrix = load_unweighted_flag(flag_file, fmt='coo')
res = flagser_unweighted(adjacency_matrix)
betti_exp = betti[os.path.split(flag_file)[1]]
betti_res = res["betti"]
assert_almost_equal(betti_res, betti_exp)
cell_count_exp = cell_count[os.path.split(flag_file)[1]]
cell_count_res = res["cell_count"]
assert_almost_equal(cell_count_res, cell_count_exp)
def compute(tribes, adj_matrix, conv, precision):
# Normalized Betti coefficients
nbcs = []
pbar = progressbar.ProgressBar()
for tribe in pbar(tribes):
# Convert from GIDs to local indexing
tribe_local_indices = conv.indices(tribe)
adj_submat = adj_matrix[np.ix_(tribe_local_indices, tribe_local_indices)]
bettinumbers = pyflagser.flagser_unweighted(adj_submat, directed=True)['betti']
cellcounts = pyflagser.flagser_unweighted(adj_submat, directed=True)['cell_count']
parameter = sum(list(map(lambda x: (x+1)*bettinumbers[x]/cellcounts[x]
if cellcounts[x] != 0 else 0,
range(min(len(bettinumbers), len(cellcounts))))))
nbcs.append(np.round(parameter,precision))
return nbcs
def compute(tribes, adj_matrix, conv, precision):
ec = []
pbar = progressbar.ProgressBar()
for tribe in pbar(tribes):
# Convert from GIDs to local indexing
tribe_local_indices = conv.indices(tribe)
adj_submat = adj_matrix[np.ix_(tribe_local_indices,
tribe_local_indices)]
ec.append(
pyflagser.flagser_unweighted(adj_submat, directed=True)['euler'])
return ec
def compute(adj_sumatrix, parameter="euler", index=None):
res = pyflagser.flagser_unweighted(adj_sumatrix, directed=True)[parameter]
if index is not None:
res = res[index]
return res
def test_huge_graphs():
"""Regression test for issue #65"""
from scipy.sparse import coo_matrix
x = coo_matrix(([1], ([0], [1])), shape=(2**16, 2**16))
flagser_unweighted(x)