Added connectivity measure suitable for testing May stability criterion

Author: highperformancecoder

models/ecolab_model.cc

@@ -376,6 +376,15 @@ EcolabPoint<B>::mutate(const E& mut_scale)
 #endif
 }
 
+// Given by the variance of the offdiagonal components, when each diagonal component is normalised to 1
+double ModelData::connectivity() const
+{
+  if (species.size()==0) return 0;
+  array<double> v=interaction.val/sqrt(interaction.diag[interaction.row]*interaction.diag[interaction.col]);
+  // divide by total number of offdiagonal components, as the data is sparse
+  return sum(v*v)/(species.size()*(species.size()-1));
+}
+
 
 /* do the offdiagonal mutations */
 void do_row_or_col(array<double>& tmp, double range, double minval, double gdist)

models/ecolab_model.h

@@ -53,6 +53,8 @@ struct ModelData
   void condense(const array<bool>& mask, size_t mask_true);
   void mutate(const array<int>&); 
   double complexity() {return ::complexity(foodweb);}
+  /// May criterion connectivity σ²
+  double connectivity() const;
 };
 
 template <class E, class P> struct RoundArray; 

models/panmictic_ecolab.py

@@ -1,5 +1,6 @@
 from ecolab_model import panmictic_ecolab as ecolab
 from random import random
+import math
 # initial number of species
 nsp=100
 
@@ -41,14 +42,16 @@ def randomList(num, min, max):
 ecolab.updateConnectionPlot()
 
 def step():
-    ecolab.generate()
+    ecolab.generate(100)
     ecolab.mutate()
     ecolab.updateConnectionPlot()
     ecolab.condense()
     nsp=len(ecolab.species)
     statusBar.configure(text=f't={ecolab.tstep()} nsp:{nsp}')
     plot('No. species',ecolab.tstep(),nsp)
     plot('Density',ecolab.tstep(),ecolab.density(), pens=ecolab.species())
+    plot('Conn-Nsp',ecolab.connectivity(),nsp)
+    plot('Conn*density',ecolab.tstep(),ecolab.connectivity()*nsp)
 
 gui(step,ecolab)