Unable to Access Predecessor Nodes in Agentpy Network

[huiziy]

Dear Developers:

Hello, first I want to thank you for creating such a fantastic package! It has helped me immensely in my research! Recently, I've had an issue accessing the predecessor nodes in the network. I've created a scale-free network (directed) using a similar code as in the tutorial (https://agentpy.readthedocs.io/en/latest/agentpy_virus_spread.html); however, my network needs to be directed so I can access the predecessors as well as the successors of each node. However, I am having trouble accessing both as an error reads: "The node Person (Obj 9) is not in the digraph." when my code is for n in self.network.graph.predecessors(self) I've also attached my full code below. Thank you so much for your help and I apologize if this seems to be a dumb question!

Best,
Huizi

# Model design
import agentpy as ap
import networkx as nx
import random
import pandas as pd
import numpy as np
from scipy.stats import truncnorm

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
import IPython

class Person(ap.Agent):

    def setup(self):
        """ Initialize a new variable at agent creation. """
        self.condition = 0  # Susceptible = 0, Infected = 1, Recovered = 2

    def being_sick(self):
        """ Spread disease to peers in the network. """
        rng = self.model.random
        for n in self.network.graph.predecessors(self):
            if n.condition == 0 and self.p.infection_chance > rng.random():
                n.condition = 1  # Infect susceptible peer
        if self.p.recovery_chance > rng.random():
            self.condition = 2  # Recover from infection

class VirusModel(ap.Model):

    def setup(self):
        """ Initialize the agents and network of the model. """

        # Prepare a scale free network
        graph = nx.scale_free_graph(
            self.p.population)

        # Create agents and network
        self.agents = ap.AgentList(self, self.p.population, Person)
        self.network = self.agents.network = ap.Network(self, graph)
        self.network.add_agents(self.agents, self.network.nodes)

        # Infect a random share of the population
        I0 = int(self.p.initial_infection_share * self.p.population)
        self.agents.random(I0).condition = 1

    def update(self):
        """ Record variables after setup and each step. """

        # Record share of agents with each condition
        for i, c in enumerate(('S', 'I', 'R')):
            n_agents = len(self.agents.select(self.agents.condition == i))
            self[c] = n_agents / self.p.population
            self.record(c)

        # Stop simulation if disease is gone
        if self.I == 0:
            self.stop()

    def step(self):
        """ Define the models' events per simulation step. """

        # Call 'being_sick' for infected agents
        self.agents.select(self.agents.condition == 1).being_sick()

    def end(self):
        """ Record evaluation measures at the end of the simulation. """

        # Record final evaluation measures
        self.report('Total share infected', self.I + self.R)
        self.report('Peak share infected', max(self.log['I']))

parameters = {
    'population': 1000,
    'infection_chance': 0.3,
    'recovery_chance': 0.1,
    'initial_infection_share': 0.1,
    'number_of_neighbors': 20,
    'network_randomness': 0.5
    #'steps':10
}

model = VirusModel(parameters)
results = model.run()