Python MultiPlot.add_plot示例

示例#1

    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)

示例#2

文件： test_viz.py 项目： GiulioRossetti/ndlib

    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)

示例#3

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)

示例#4

 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)

示例#5

文件： 18-02-network-diffusion.py 项目： Monicajia/mybook

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)

示例#6

# 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)

#%%
# 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

示例#7

文件： 18-02-network-diffusion.py 项目： Monicajia/mybook

show(p)


# In[7]:


viz2 = DiffusionPrevalence(model, trends)
p2 = viz2.plot(width=400, height=400)
show(p2)


# In[8]:


vm = MultiPlot()
vm.add_plot(p)
vm.add_plot(p2)
m = vm.plot()
show(m)


# ### Kertesz Threshold model
# 
# The Kertesz Threshold model was introduced in 2015 by `Ruan` et al. [1] and it is an extension of the Watts threshold model [2].
# 
# We set the initial infected as well blocked node sets equals to the 10% of the overall population, assign a threshold of 0.25 to all the nodes and impose an probability of spontaneous adoptions of 40%.
# 
# - [1]	Z. Ruan, G. In ̃iguez, M. Karsai, and J. Kertész, “Kinetics of social contagion,” Phys. Rev. Lett., vol. 115, p. 218702, Nov 2015.
# - [2]	D.J. Watts, “A simple model of global cascades on random networks,” Proceedings of the National Academy of Sciences, vol. 99, no. 9, pp. 5766–5771, 2002.

# In[18]:

示例#8

import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics as ep
from bokeh.io import show
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
from ndlib.viz.bokeh.MultiPlot import MultiPlot

# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)

# Model selection
model = ep.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(10)
trends = model.build_trends(iterations)
viz1 = DiffusionTrend(model, trends)
p = viz1.plot(width=400, height=400)
vm1 = MultiPlot()
vm1.add_plot(p)
m = vm1.plot()
show(m)