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) #%%