def test_multi(self):
vm = MultiPlot()
g = nx.erdos_renyi_graph(1000, 0.1)
model = sir.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 = sir.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_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 draw_epidemic_plot(model, trends):
viz = DiffusionTrend(model, trends)
p = viz.plot(width=650, height=500)
viz2 = DiffusionPrevalence(model, trends)
p2 = viz2.plot(width=650, height=500)
vm = MultiPlot()
vm.add_plot(p)
vm.add_plot(p2)
m = vm.plot()
show(m)
def plot_trends(self,iterations):
if self.config["UI"].getboolean("verbose"):
print("Qu: Plotting trends ... ")
trends = self._active_model.build_trends(iterations)
viz = DiffusionTrend(self._active_model, trends)
p = viz.plot(width=400, height=400)
viz2 = DiffusionPrevalence(self._active_model, trends)
p2 = viz2.plot(width=400, height=400)
vm = MultiPlot()
vm.add_plot(p)
vm.add_plot(p2)
m = vm.plot()
show(m)
def plot_diffusion(model, iterations):
output_notebook() # show bokeh in notebook
trends = model.build_trends(iterations)
viz = DiffusionTrend(model, trends)
p = viz.plot(width=400, height=400)
viz2 = DiffusionPrevalence(model, trends)
p2 = viz2.plot(width=400, height=400)
vm = MultiPlot()
vm.add_plot(p)
vm.add_plot(p2)
m = vm.plot()
show(m)
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)
#%%
# pip install ndlib
import networkx as nx
import ndlib.models.epidemics.SIRModel as sir
# Network Definition
g = nx.erdos_renyi_graph(1000, 0.1)
# Model Selection
model = sir.SIRModel(g)
import ndlib.models.ModelConfig as mc
# Model Configuration
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('gamma', 0.01)
# Model selection
model = sis.SISModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter('lambda', 0.005)
cfg.add_model_parameter("fraction_infected", 0.05)
model.set_initial_status(cfg)
#%%
# Simulation
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
from bokeh.io import output_notebook, show
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
viz = DiffusionTrend(model, trends)
p = viz.plot(width=400, height=400)
# show(p)
from ndlib.viz.bokeh.DiffusionPrevalence import DiffusionPrevalence
viz2 = DiffusionPrevalence(model, trends)
p2 = viz2.plot(width=400, height=400)
# show(p2)
from ndlib.viz.bokeh.MultiPlot import MultiPlot
vm = MultiPlot()
vm.add_plot(p)
vm.add_plot(p2)
m = vm.plot()
show(m)
#%%
# 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)
#%%