def final_results():
for day in days:
path = 'Data/Preprocessed/preprocessed_' + day
graph = dn.read_snapshots(path, nodetype=int, timestamptype=int)
d = Day(graph)
i_total = np.empty((alpha.shape[0], n)) # alpha x simulations
i_total = d.simulate(alpha, n, final_plot=False)
with open('Data/Simulations/final_' + day + '.pkl', 'wb') as output:
pickle.dump(i_total, output, pickle.HIGHEST_PROTOCOL)
print('finished', n, 'simulations for day', day)
def preprocess(windows):
data = {}
for filename in os.listdir('Data/Preprocessed'):
if 'preprocessed_0' in filename:
g = dn.read_snapshots(str('Data/Preprocessed/' + filename), nodetype=int, timestamptype=int)
day = filename[13:18]
print(day)
data[day] = burstiness_window(g, windows)
with open('Data/Preprocessed_bursts.txt', 'w') as outfile:
json.dump(data, outfile)
def test_snapshot_graph_flag(self):
g = dn.DynGraph()
g.add_interaction(1, 2, 2)
g.add_interaction(1, 2, 2, e=6)
g.add_interaction(1, 2, 7, e=11)
g.add_interaction(1, 2, 8, e=15)
g.add_interaction(1, 2, 18)
g.add_interaction(1, 2, 19)
dn.write_snapshots(g, "test4.txt", delimiter=" ")
h = dn.read_snapshots("test4.txt", nodetype=int, timestamptype=int, keys=True)
# self.assertEqual(list(g.stream_interactions()), list(h.stream_interactions()))
self.assertEqual(g.number_of_interactions(), h.number_of_interactions())
os.remove("test4.txt")
def load_graphs(dir='Data/Preprocessed/'):
"""
Construct dictionary containing results of may indexed by the day month (e.g. 05-09)
"""
all_days = {}
for filename in os.listdir(dir):
if 'preprocessed_' in filename:
path = dir + filename
all_days[filename[13:]] = dn.read_snapshots(path,
nodetype=int,
timestamptype=int)
print("Loaded", len(all_days), "days.")
return all_days
def read_graph(day):
path = "Data/Preprocessed/preprocessed_" + day
graph = dn.read_snapshots(path, nodetype=int, timestamptype=int)
return graph
'node_size': 1,
'line_color': 'red',
'linewidths': 0,
'width': 0.1,
}
nx.draw_circular(G, **options)
plt.show()
G=nx.read_edgelist("/Users/jennie/Documents/analytics/xanax/xanshort.csv", delimiter=",")
import networkx as nx
import dynetx as dn
import matplotlib.pyplot as plt
g = dn.DynGraph(edge_removal=False)
g = dn.read_snapshots("/Users/jennie/Documents/analytics/xanax/lyrics_ngrams.txt", timestamptype=int)
pos=nx.spring_layout(g,scale=20) # double distance between all nodes
plt.figure(3,figsize=(10,10))
nx.draw(g,pos,node_size=10, font_size=8, with_labels=True)
list(g.stream_interactions())
import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.opinions.AlgorithmicBiasModel as ab
# Network topology
g=nx.read_edgelist("/Users/jennie/Documents/analytics/xanax/lyrics_ngram.csv", delimiter=",")
