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SIR_model.py
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SIR_model.py
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"""
Example of ModECI MDF - SIR model
An SIR model is an epidemiological model that computes the theoretical number of people infected with a contagious illness in a closed population over time. The name of this class of models derives from the fact that they involve coupled equations relating the number of susceptible people S(t), number of people infected I(t), and number of people who have recovered R(t).
"""
from modeci_mdf.mdf import*
import matplotlib.pyplot as plt
import os
import sys
from matplotlib.animation import FuncAnimation
import imageio
import numpy as np
# Rest of the code remains the same
def main(total_population=1000, initial_infected=1, initial_recovered=0, beta=0.3, gamma=0.1, mode=None):
# Initialize the Model
sir_model = Model(id="SIR_Model")
# Create a Graph within the Model
sir_graph = Graph(id="SIR_Graph")
sir_model.graphs.append(sir_graph)
# # Parameters for the model
# total_population = 1000
# initial_infected = 1
# initial_recovered = 0
# beta = 0.3 # Infection rate
# gamma = 0.1 # Recovery rate
initial_susceptible = total_population - initial_infected - initial_recovered
# SIR equation Node
sir_node = Node(id="id")
total_population = Parameter(id="total_population", value=total_population)
gamma = Parameter(id="gamma", value=gamma)
beta = Parameter(id="beta", value=beta)
susceptible_population = Parameter(id="susceptible_population",
default_initial_value=initial_susceptible,
time_derivative="-beta*susceptible_population*infected_population/total_population"
)
infected_population = Parameter(id="infected_population",
default_initial_value=initial_infected,
time_derivative="beta*susceptible_population*infected_population/total_population - gamma*infected_population"
)
recovered_population = Parameter(id="recovered_population",
default_initial_value=initial_recovered,
time_derivative="gamma*infected_population"
)
infected_output1 = OutputPort(id="out_port1",value=susceptible_population.id)
infected_output2 = OutputPort(id="out_port2",value=infected_population.id)
infected_output3 = OutputPort(id="out_port3",value=recovered_population.id)
sir_node.parameters.append(gamma)
sir_node.parameters.append(beta)
sir_node.parameters.append(total_population)
sir_node.parameters.append(susceptible_population)
sir_node.parameters.append(infected_population)
sir_node.parameters.append(recovered_population)
sir_node.output_ports.append(infected_output1)
sir_node.output_ports.append(infected_output2)
sir_node.output_ports.append(infected_output3)
# Recovered Node
recovered_node = Node(id="Recovered")
recovered_input = InputPort(id="input_port")
recovered_output = OutputPort(id="out_port",value=recovered_input.id)
recovered_node.input_ports.append(recovered_input)
recovered_node.output_ports.append(recovered_output)
#Infected Node
infected_node = Node(id="Infected")
infected_input = InputPort(id="input_port")
infected_output = OutputPort(id="out_port",value=infected_input.id)
infected_node.input_ports.append(infected_input)
infected_node.output_ports.append(infected_output)
#Infected Node
susceptible_node = Node(id="Susceptible")
susceptible_input = InputPort(id="input_port")
susceptible_output = OutputPort(id="out_port",value=susceptible_input.id)
susceptible_node.input_ports.append(susceptible_input)
susceptible_node.output_ports.append(susceptible_output)
# Add nodes to the graph
sir_graph.nodes.append(sir_node)
sir_graph.nodes.append(recovered_node)
sir_graph.nodes.append(infected_node)
sir_graph.nodes.append(susceptible_node)
# Infected to Recovered transition
sir_to_rec_edge = Edge(
id="sir_to_rec",
sender=sir_node.id,
sender_port="out_port3",
receiver=recovered_node.id,
receiver_port="input_port",
)
sir_to_inf_edge = Edge(
id="sir_to_inf",
sender=sir_node.id,
sender_port="out_port2",
receiver=infected_node.id,
receiver_port="input_port",
)
sir_to_sus_edge = Edge(
id="sir_to_sus",
sender=sir_node.id,
sender_port="out_port1",
receiver=susceptible_node.id,
receiver_port="input_port",
)
# Add edges to the graph
sir_graph.edges.append(sir_to_rec_edge)
sir_graph.edges.append(sir_to_inf_edge)
sir_graph.edges.append(sir_to_sus_edge)
if mode=="run":
from modeci_mdf.execution_engine import EvaluableGraph
eg = EvaluableGraph(sir_graph, verbose=False)
eg.evaluate()
dt = 1
duration = 100
t = 0
times = []
s = []
i = []
r = []
while t <= duration:
times.append(t)
print("====== Evaluating at t = %s ======" % (t))
if t == 0:
eg.evaluate()
else:
eg.evaluate(time_increment=dt)
s.append(eg.enodes["id"].evaluable_outputs["out_port1"].curr_value)
i.append(eg.enodes["id"].evaluable_outputs["out_port2"].curr_value)
r.append(eg.enodes["id"].evaluable_outputs["out_port3"].curr_value)
t += dt
print('Susceptible polution: %s'%eg.enodes["id"].evaluable_outputs["out_port1"].curr_value)
print('Infected polution: %s'%eg.enodes["id"].evaluable_outputs["out_port2"].curr_value)
print('Recovered polution: %s'%eg.enodes["id"].evaluable_outputs["out_port3"].curr_value)
# Create subplots
fig1, axs = plt.subplots(1, 3, figsize=(15, 5),sharey=True)
# Plotting Susceptible population
axs[0].plot(times, s, label='Susceptible', color='blue')
axs[0].set_xlabel('Time')
axs[0].set_ylabel('Susceptible Population')
axs[0].set_title('Susceptible Population over Time')
axs[0].legend()
axs[0].grid(True)
# Plotting Infected population
axs[1].plot(times, i, label='Infected', color='orange')
axs[1].set_xlabel('Time')
axs[1].set_ylabel('Infected Population')
axs[1].set_title('Infected Population over Time')
axs[1].legend()
axs[1].grid(True)
# Plotting Recovered population
axs[2].plot(times, r, label='Recovered', color='green')
axs[2].set_xlabel('Time')
axs[2].set_ylabel('Recovered Population')
axs[2].set_title('Recovered Population over Time')
axs[2].legend()
axs[2].grid(True)
plt.tight_layout() # Adjust layout to prevent overlap
plt.show()
# Prepare an array to hold the rendered frames
frames = []
# Generate frames
for frame in range(len(times)):
fig, ax = plt.subplots()
ax.plot(times[:frame+1], s[:frame+1], label='Susceptible', color='blue')
ax.plot(times[:frame+1], i[:frame+1], label='Infected', color='orange')
ax.plot(times[:frame+1], r[:frame+1], label='Recovered', color='green')
ax.xaxis.set_major_locator(plt.MaxNLocator(6))
ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: '{:.1f}'.format(x)))
ax.set_xlabel('Time')
ax.set_ylabel('Population')
ax.set_title('Population over time')
ax.legend()
ax.grid(True)
# Convert the Matplotlib figure to a RGB array and close the figure to free memory
fig.canvas.draw()
image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
frames.append(image)
plt.close(fig)
gif_path = 'animated_plot.gif'
# Save the frames as a GIF
imageio.mimsave(gif_path, frames, fps=20)
# # Create an animated line graph
# fig2, ax = plt.subplots()
# ax.plot(times, s, label='Susceptible', color='blue')
# ax.plot(times, i, label='Infected', color='orange')
# ax.plot(times, r, label='Recovered', color='green')
# ax.xaxis.set_major_locator(plt.MaxNLocator(6))
# ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: '{:.1f}'.format(x)))
# ax.set_xlabel('Time')
# ax.set_ylabel('Population')
# ax.set_title('Population over time')
# ax.legend()
# ax.grid(True)
# def animate(frame):
# ax.clear()
# ax.plot(times[:frame+1], s[:frame+1], label='Susceptible', color='blue')
# ax.plot(times[:frame+1], i[:frame+1], label='Infected', color='orange')
# ax.plot(times[:frame+1], r[:frame+1], label='Recovered', color='green')
# ax.xaxis.set_major_locator(plt.MaxNLocator(6))
# ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: '{:.1f}'.format(x)))
# ax.set_xlabel('Time')
# ax.set_ylabel('Population')
# ax.set_title('Population over time')
# ax.legend()
# ax.grid(True)
# return[ax]
# anim = FuncAnimation(fig2, animate, frames=times, interval=50, blit=True)
# # Save the animated plot
# anim.save('animated_plot.gif', writer='imagemagick')
# plt.show()
# return [fig1, anim]
return [fig1, gif_path]
elif mode=="graph":
sir_model.to_graph_image(
engine="dot",
output_format="png",
view_on_render=False,
level=3,
filename_root="sir_model",
is_horizontal=True
)
from IPython.display import Image
Image(filename="sir_model.png")
image_path = "sir_model.png"
return image_path
return sir_graph
if __name__ == "__main__":
# Check if there are any command line arguments
if len(sys.argv) > 1:
# Assuming the second argument is the mode (e.g., '-run' or '-graph')
mode_arg = sys.argv[1]
if mode_arg == "-run":
main(mode="run")
elif mode_arg == "-graph":
main(mode="graph")
else:
print("Invalid argument. Please use '-run' or '-graph'.")
else:
print("No arguments provided. Please specify '-run' or '-graph'.")