def test_multi(self):
vm = MultiPlot()
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.SIRModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('gamma', 0.01)
config.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(config)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
viz = DiffusionTrend(model, trends)
p = viz.plot()
vm.add_plot(p)
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.SIRModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('gamma', 0.01)
config.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(config)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
viz = DiffusionPrevalence(model, trends)
p1 = viz.plot()
vm.add_plot(p1)
m = vm.plot()
self.assertIsInstance(m, Column)
def test_visualize(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.SIRModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('gamma', 0.01)
config.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(config)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
viz = DiffusionTrend(model, trends)
p = viz.plot()
self.assertIsInstance(p, Figure)
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.SISModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter('proability of return to S stage', 0.005)
cfg.add_model_parameter("initial infected rate", 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)
# Model selection
model = si.SIModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(cfg)
# Simulation execution
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
from bokeh.io import output_notebook, show
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
viz1 = DiffusionTrend(model, trends)
p = viz1.plot(width=400, height=400)
# show(p)
print(viz1)
from ndlib.viz.bokeh.DiffusionPrevalence import DiffusionPrevalence
viz2 = DiffusionPrevalence(model, trends)
p2 = viz2.plot(width=400, height=400)
from ndlib.viz.bokeh.MultiPlot import MultiPlot
vm1 = MultiPlot()
vm1.add_plot(p)
vm1.add_plot(p2)
m = None
m = vm1.plot()
show(m)
#%%
model.set_initial_status(config) # In[4]: # Simulation iterations = model.iteration_bunch(200) trends = model.build_trends(iterations) # In[6]: output_notebook() # show bokeh in notebook viz = DiffusionTrend(model, trends) p = viz.plot(width=400, height=400) show(p) # In[7]: viz2 = DiffusionPrevalence(model, trends) p2 = viz2.plot(width=400, height=400) show(p2) # In[8]:
import networkx as nx
import ndlib.models.epidemics as ep
from bokeh.io import output_notebook, show
import ndlib.models.ModelConfig as mc
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
import csv
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model Selection
sis_model = ep.SISModel(g)
# Model Configuration
config = mc.Configuration()
config.add_model_parameter('beta', 0.005)
config.add_model_parameter('lambda', 0.01)
config.add_model_parameter("percentage_infected", 0.01)
sis_model.set_initial_status(config)
# Simulation
iterations = sis_model.iteration_bunch(100)
trends = sis_model.build_trends(iterations)
viz = DiffusionTrend(sis_model, trends)
p = viz.plot(width=400, height=400)
show(p)
