<jupyter_start><jupyter_text>9 basins 1 D<jupyter_code>microstates = {}

with open('trad_aux.aux', 'r') as f:
    for line in f:
        fields=line.split()
        microstates[float(fields[1])]=int(fields[0])-1

net = okn.Network(microstate_names=microstates)

with open('net.oup', 'r') as f:
    for line in f:
        fields=line.split()
        origin = int(fields[0])-1
        end = int(fields[1])-1
        weight = float(fields[2])
        net.add_transition(origin, end, weight=weight)
        
net.update_probabilities()
net.info()
mwns = okn.most_weighted_nodes(net, top=3)

for node in mwns:
    print(node.index, node.microstate.name, node.weight, node.probability)
net.symmetrize()
mwns = okn.most_weighted_nodes(net, top=3)

for node in mwns:
    print(node.index, node.microstate.name, node.weight, node.probability)
gm = okn.global_minimum(net)

print(gm.index, gm.microstate.name, gm.weight, gm.probability)
lms = okn.local_minima(net)

for node in lms:
    print(node.index, node.microstate.name, node.weight, node.probability)
okn.components(net)
okn.landscapes.bottom_up_1D(net)<jupyter_output><empty_output><jupyter_text>xx=numpy.zeros(shape=net.num_nodes,dtype=float)yy=numpy.zeros(shape=net.num_nodes,dtype=float)for ii in range(net.num_nodes):    xx[ii]=float(net.node[ii].label)    yy[ii]=float(net.node[ii].weight)net.gradient_clusters_2()xx_yy_cluster=[] for ii in range(net.num_clusters):    palcluster=[]    for jj in net.cluster[ii].nodes:        palcluster.append([float(net.node[jj].label),net.node[jj].weight])    palcluster=numpy.array(palcluster)    xx_yy_cluster.append(palcluster)net.dento_tiempo()net.dendo_bottom_up()<jupyter_code>aa=list(3)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14962010520163646, 'fraction_numerical': 0.008766803039158387, 'mean_word_length': 5.028169014084507, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '544359', 'license': 'MIT', 'n_tokens_mistral': 676, 'n_tokens_neox': 659, 'n_words': 96, 'path': 'examples/9_basins_1D/analysis_net.ipynb', 'repo_name': 'uibcdf/OpenKinNet', 'size': '1711'}