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)