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))