Initial subgraphing work

Analysis/ode.R

@@ -1,7 +1,7 @@
 ## Set Working Directory to file directory - RStudio approach
 setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
 
-weighted <- 'False'
+weighted <- 'True'
 #conda_install(envname = "r-reticulate", packages="networkx")
 #conda_install(envname = "r-reticulate", packages="matplotlib")
 #conda_install(envname = "r-reticulate", packages="pydot")

Analysis/prep_model.py

@@ -36,6 +36,7 @@ def prep_seirds(weighted):
   
   ep_tmp = 0 # counter for epsilon
   inf_ct = 0 # infection rate counter
+  recov_ct = 0 # recov rate counter
   
   for node in A:
       color = A.get_node(node).attr.to_dict()['fillcolor']
@@ -50,16 +51,18 @@ def prep_seirds(weighted):
           in_edges = list(G.in_edges(node))
           out_edges = list(G.out_edges(node))
           
-          
           tmp_S = 1
+          tmp_recov = 0
           tmp_inf = 0
           for source in in_edges:
               tmp_S = 1
               # If previous node was infected, then we are recovered
               if (color_d[source[0]] == 'red'):
-                  R = R + 1
-                  tmp_S = 0
-                  break # No need to check the other nodes
+                if(weighted == 'False' or not in_edges):
+                  tmp_recov = 1
+                else:
+                  recov_ct = recov_ct + 1/len(in_edges) # trivial weighting
+          recov_ct = recov_ct + tmp_recov
           for source in out_edges:
             if (color_d[source[0]] == 'red'):
              if(weighted == 'False' or not out_edges):
@@ -68,26 +71,30 @@ def prep_seirds(weighted):
                 inf_ct = inf_ct + 1/len(out_edges) # trivial weighting
           inf_ct = inf_ct + tmp_inf
           S = S + tmp_S
-              #G[source[0]][node]['weight'] = 3 
       elif color == 'yellow':
           color_map.append(color)
           color_d[node] = color
           in_edges = list(G.in_edges(node))
+          
           tmp_E = 1
           tmp_R = 0
           tmp_inf = 0
+          tmp_recov = 0
           for source in in_edges:
               # If previous node was infected, then we are recovered
               if (color_d[source[0]] == 'red'):
-                  tmp_R = 1
                   tmp_E = 0
+                  if(weighted == 'False' or not in_edges):
+                    tmp_recov = 1
+                  else:
+                    recov_ct = recov_ct + 1/len(in_edges) # trivial weighting
               if (color_d[source[0]] == '' or color_d[source[0]] == 'white'):
                   if(weighted == 'False' or not in_edges):
                     tmp_inf = 1 # add 1 for the inf counter
                   else:
                     inf_ct = inf_ct + 1/len(in_edges) # trivial weighting
           E = E + tmp_E
-          R = R + tmp_R
+          recov_ct = recov_ct + tmp_recov
           inf_ct = inf_ct + tmp_inf
       else:
           color_map.append(color)
@@ -116,7 +123,8 @@ def prep_seirds(weighted):
   # Params
   beta = (inf_ct)/len(A) # rate of infec
   delta = 1 # incubation period
-  gamma_r = R/len(A) # recov rate
+  #gamma_r = R/len(A) # recov rate
+  gamma_r = recov_ct/len(A)
   gamma_d = D/len(A) # death rate
   mu = D/(I_R+I_D) # fatality ratio
   epsilon = ep_tmp/len(A) # infected import rate

Analysis/segment_network.py

@@ -0,0 +1,28 @@
+#!/usr/bin/python3 
+
+import networkx as nx
+import matplotlib.pyplot as plt
+from collections import OrderedDict
+from operator import getitem
+import itertools, os, sys
+
+A = nx.drawing.nx_agraph.to_agraph(nx.drawing.nx_pydot.read_dot("./1_mo_color_DOTFILE.dot"))
+A.layout('dot')
+#A.draw('tree.png')
+A.remove_node('\\n') # Remove "newline" node from newline end of dot file
+G=nx.DiGraph(A)
+
+print(A.get_edge_data(0, 1))
+
+subgraph = []
+to_explore = []
+to_explore.append('0')
+
+for node in to_explore:
+  for edge in list(G.out_edges(node)):
+    print(edge)
+print()
+
+for u, v, d in G.edges(data=True):
+    print(u, v, d['label'])  
+#for node in A: