def test_trend_comparison(self):
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = epd.SIRModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter('gamma', 0.02)
cfg.add_model_parameter("percentage_infected", 0.01)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
model1 = epd.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter("percentage_infected", 0.01)
model1.set_initial_status(cfg)
iterations = model1.iteration_bunch(200)
trends1 = model1.build_trends(iterations)
viz = DiffusionTrendComparison([model, model1], [trends, trends1])
viz.plot("trend_comparison.pdf")
os.remove("trend_comparison.pdf")
def test_multiple_si_model(self):
for g in get_graph(True):
model = epd.SIModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.01)
config.add_model_parameter("fraction_infected", 0.1)
model.set_initial_status(config)
executions = ut.multi_runs(model, execution_number=10, iteration_number=50)
self.assertEqual(len(executions), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_si_model(self):
for g in get_graph(True):
model = epd.SIModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.5)
config.add_model_parameter("fraction_infected", 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_si_model(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.SIModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.5)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_multiple_si_model(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.SIModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.01)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
executions = ut.multi_runs(model,
execution_number=10,
iteration_number=50)
self.assertEqual(len(executions), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def count(g, beta=0.2, percentage_infected=0.01, estimations=10):
len_nodes = len(g.nodes())
list_of_iter = []
for i in range(estimations):
model = ep.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', beta)
cfg.add_model_parameter("percentage_infected", percentage_infected)
model.set_initial_status(cfg)
iteration = model.iteration() # initialization
while (iteration['node_count'][1] < len_nodes):
iteration = model.iteration()
list_of_iter.append(iteration['iteration'])
return np.mean(list_of_iter)
import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics as ep
from bokeh.io import output_notebook, show
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
# Network topology
g = nx.erdos_renyi_graph(1000, 0.05)
# Model selection
model = ep.SIModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter("fraction_infected", 0.05)
model.set_initial_status(cfg)
# Simulation execution
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
#visualize
viz = DiffusionTrend(model, trends)
p = viz.plot(width=550, height=550)
show(p)
def get_sznajd_params():
print('\n\tInput SZNAJD model parameters')
fraction_infected = float(input('fraction_infected(initial)[0, 1]: '))
cfg = mc.Configuration()
cfg.add_model_parameter('fraction_infected', fraction_infected) # 0.1
return cfg
if __name__ == '__main__':
g = nx.erdos_renyi_graph(1000, 0.1)
num_iterations = int(input('Number of iterations for all models: ')) # 200
###############################################################
SIModel = ep.SIModel(g.copy())
SIModel.set_initial_status(get_si_params())
SI_iterations = SIModel.iteration_bunch(num_iterations)
SI_trends = SIModel.build_trends(SI_iterations)
visualize(SIModel, SI_trends, sub_dir='epidemics')
SISModel = ep.SISModel(g.copy())
SISModel.set_initial_status(get_sis_params())
SIS_iterations = SISModel.iteration_bunch(num_iterations)
SIS_trends = SISModel.build_trends(SIS_iterations)
visualize(SISModel, SIS_trends, sub_dir='epidemics')
SEISModel = ep.SEISModel(g.copy())
SEISModel.set_initial_status(get_seis_params())
SEIS_iterations = SEISModel.iteration_bunch(num_iterations)
SEIS_trends = SEISModel.build_trends(SEIS_iterations)
