115 lines
3.1 KiB
Python
115 lines
3.1 KiB
Python
#!/usr/bin/python3
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import networkx as nx
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import matplotlib.pyplot as plt
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from collections import OrderedDict
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from operator import getitem
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import itertools, os, sys
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# Change dir to location of this python file
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print(os.getcwd())
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#os.chdir(os.path.dirname(sys.argv[0]))
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#print(os.getcwd())
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# AGraph preserves attributes, networkx Graph does not.
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# Many of the desired functions are in networkx.
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# So import AGraph to keep attributes, then convert to Networkx.
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A = nx.drawing.nx_agraph.to_agraph(nx.drawing.nx_pydot.read_dot("./1_mo_color_DOTFILE.dot"))
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A.layout('dot')
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#A.draw('tree.png')
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A.remove_node('\\n') # Remove "newline" node from newline end of dot file
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G=nx.DiGraph(A)
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color_map = []
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color_d = {}
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node_pos = {} # used for drawing/graphing
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# Compartments
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S = 0
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I_R = 0
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I_D = 0
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E = 0
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R = 0
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D = 0
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ep_tmp = 0 # counter for epsilon
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for node in A:
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color = A.get_node(node).attr.to_dict()['fillcolor']
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str_pos = A.get_node(node).attr.to_dict()['pos']
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coords = str_pos.split(',')
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x = coords[0] # layout for draw function
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y = coords[1]
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node_pos[node] = float(x), float(y)
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if color is None or color == '':
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color_map.append("white")
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color_d[node] = color
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in_edges = list(G.in_edges(node))
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tmp_S = 1
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for source in in_edges:
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tmp_S = 1
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# If previous node was infected, then we are recovered
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if (color_d[source[0]] == 'red'):
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R = R + 1
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tmp_S = 0
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break # No need to check the other nodes
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S = S + tmp_S
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#G[source[0]][node]['weight'] = 3
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elif color == 'yellow':
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color_map.append(color)
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color_d[node] = color
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in_edges = list(G.in_edges(node))
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tmp_E = 1
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for source in in_edges:
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tmp_E = 1
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# If previous node was infected, then we are recovered
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if (color_d[source[0]] == 'red'):
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R = R + 1
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tmp_E = 0
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break # No need to check the other nodes
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E = E + tmp_E
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else:
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color_map.append(color)
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color_d[node] = color
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# Check if node dies
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out_edges = list(G.out_edges(node))
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if not out_edges:
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D = D + 1
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I_D = I_D + 1
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else:
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I_R = I_R + 1
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# Check if imported
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in_edges = list(G.in_edges(node))
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if not in_edges:
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ep_tmp = ep_tmp + 1
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# Params
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beta = (I_R+I_D)/len(A) # rate of infec (I/total?)
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#delta = E/len(A) # symptom appearance rate (E/total?)
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delta = 1 # incubation period
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gamma_r = R/len(A) # recov rate (R/total?)
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gamma_d = D/len(A) # death rate (D/total?)
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mu = D/(I_R+I_D) # fatality ratio (D/I)
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epsilon = ep_tmp/len(A) # infected import rate
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omega = 1 # waning immunity rate
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print("Model Compartments:")
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print("S:", str(S))
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print("E:", str(E))
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print("I_R:", str(I_R))
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print("I_D:", str(I_D))
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print("R:", str(R))
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print("D:", str(D))
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print("\n")
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print("Model Parameters:")
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print("beta:", str(beta))
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print("delta:", str(delta))
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print("gamma_r:", str(gamma_r))
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print("gamma_d:", str(gamma_d))
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print("mu:", str(mu))
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print("epsilon:", str(epsilon))
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print("omega:", str(omega))
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