def gs2dgms_parallel(n_jobs=1, **kwargs):
""" a wraaper of g2dgm for parallel computation
sync with the same-named function in fil.py
put here for parallelizaiton reason
"""
if dgms_dir_test(
**kwargs
)[1]: #and kwargs.get('ntda', None)!=True: # load only when ntda=False
dgms = load_dgms(**kwargs)
return dgms
try:
assert 'gs' in globals().keys()
except AssertionError:
print(globals().keys())
try:
# print('in gs2dgms_parallel', kwargs)
dgms = Parallel(n_jobs=n_jobs)(delayed(g2dgm)(i, gs[i], **kwargs)
for i in range(len(gs)))
except NameError: # name gs is not defined
sys.exit('NameError and exit')
save_dgms(dgms, **kwargs)
return dgms
def gs2dgms_parallel(n_jobs=1, **kwargs):
""" a wraaper of g2dgm for parallel computation """
if dgms_dir_test(**kwargs)[1]:
dgms = load_dgms(**kwargs)
return dgms
try:
assert 'gs' in globals().keys()
except AssertionError:
print(globals().keys())
dgms = Parallel(n_jobs=n_jobs)(delayed(g2dgm)(i, gs[i], **kwargs)
for i in range(len(gs)))
save_dgms(dgms, **kwargs)
return dgms
def _gs2dgms_parallel(n_jobs=1, **kwargs):
""" a wraaper of g2dgm for parallel computation """
if dgms_dir_test(**kwargs)[1]:
dgms = load_dgms(**kwargs)
return dgms
try:
assert 'gs' in globals().keys()
except AssertionError:
print(globals().keys())
try:
dgms = Parallel(n_jobs=n_jobs, backend='multiprocessing')(
delayed(g2dgm)(i, gs[i], **kwargs) for i in range(len(gs)))
except NameError: # name gs is not defined
print('NameError and exit')
sys.exit()
save_dgms(dgms, **kwargs)
return dgms
fil_d='sup',
norm=norm,
graph=graph,
ntda=ntda)
epddgms = gs2dgms_parallel(n_jobs=-1,
fil=fil,
one_hom=True,
norm=norm,
graph=graph,
ntda=ntda)
sys.exit()
sys.exit()
save_dgms(subdgms, **kwargs)
dgms_summary(subdgms)
debug(subdgms, 'subdgms')
g = nx.random_geometric_graph(100, 0.4)
print(edgefeat(g, fil='jaccard'))
np.random.seed(42)
n_node = 20
g = nx.random_geometric_graph(n_node, 0.5, seed=42)
diagram = node_fil(g, fil='hop', norm=True, base=0)
print(diagram)
# node feat example
nodefeat = np.array(list(dict(nx.degree(g)).values())).reshape(
len(g), 1) # np.random.random((n_node, 1))
nonfeat = nodefeat / float(max(nodefeat))