diff --git a/showgraphs.m b/showgraphs.m
index 421c845e92f2a6508a652a9a92d23f9a2141a10e..593de9225e4d6c401516efe9871c65edc9e4a200 100644
--- a/showgraphs.m
+++ b/showgraphs.m
@@ -22,6 +22,8 @@ Needs["ErrorBarPlots`"]
 (*   (b) How often does this procedure work/fail. Might still be faster to do switchings from Erdos-Gallai.*)
 (*   (c) Compare two greedy ways: (c1) first take highest and finish all its pairings  (c2) take new highest after every single pairing*)
 (*   (d) Time evolution for GCM on top of Erdos-Gallai time evolution.*)
+(* The initial #triangles in GCM2 is somewhere between 0 and 5 standard deviations below the average #triangles, whereas the #triangles in Erdos-Gallai can be as high as 100 standard deviations above the average.*)
+(* TODO: Not only compare number of standard deviations but also percentage above/below average.*)
 (**)
 (*- Count #moves that result in +-k triangles (one move could create many triangles at once!)*)
 (**)
@@ -43,13 +45,20 @@ Needs["ErrorBarPlots`"]
 (*- Compute  Sum over i<j<k  of  (1-Exp[- d_i d_j / (2E)]) * (1 - Exp[-d_j d_k / (2E)]) * (1 - Exp[-d_k d_i / (2E)]) .*)
 (*  Computing the f(i,j) = (1-Exp[..]) terms is fine, but then computing Sum[ f(i,j) f(j,k) f(i,k) ) ] over n^3 entries is very slow.*)
 (*  *)
-(*  - Improve runtime*)
+(*- Improve runtime*)
 (*   (a) Don't remove/add edges from the std::vector. Simply replace them. Done, is way faster for large n.*)
 (*   (b) Do not choose the three permutations with 1/3 probability: choose the "staying" one with zero probability. Should still be a valid switch chain?*)
 (*   *)
-(*   - Experimental mixing time as function of n. At (n,tau)=(1000,2.5) it seems to be between 10.000 and 20.000 steps.*)
-(*     Done. Seems to be something like  (1/2)(32-26(tau-2))n  so we run it for that time without the factor (1/2).*)
-(*  *)
+(*- Experimental mixing time as function of n. At (n,tau)=(1000,2.5) it seems to be between 10.000 and 20.000 steps.*)
+(*   Done. Seems to be something like  (1/2)(32-26(tau-2))n  so we run it for that time without the factor (1/2).*)
+(**)
+(*- GCM1: Greedy Configuration Model 1: take highest degree and completely do all its pairings (at random)*)
+(*- GCM2: Greedy Configuration Model 2: take highest degree and do a single pairing, then take new highest degree. So this matters mostly if there are multiple high degree nodes*)
+(*The success rates, conditioned on the degree sequence being graphical, is almost always higher using GCM2. For certain degree sequences the success rate of GCM2 can be 0.9 higher than that of GCM1. (i.e. amost always works vs almost always fails).*)
+(*For tau > ~2.3 the success rate of GCM2 seems to be higher than 80% for most sequences.*)
+(*For tau < ~2.3 the success rate of GCM2 can drop to less than 10% for some sequences but for many sequences it is still larger than 80%.*)
+(**)
+(**)
 (*  *)
 
 
@@ -71,11 +80,7 @@ Grid[Partition[gs,10],Frame->All]
 
 
 (* ::Section:: *)
-(*Plot triangle counts*)
-
-
-(* ::Subsection:: *)
-(*Data import and data merge*)
+(*Data import*)
 
 
 gsraw=Import[NotebookDirectory[]<>"data/graphdata.m"];
@@ -88,11 +93,19 @@ nlabels=Map["n = "<>ToString[#]&,gdata[[1,All,1,1,1]]];
 taulabels=Map["tau = "<>ToString[#]&,gdata[[All,1,1,1,2]]];
 
 
+(* ::Subsection:: *)
+(*Merge data*)
+
+
 newData=Import[NotebookDirectory[]<>"data/graphdata_3.m"];
 mergedData=Import[NotebookDirectory[]<>"data/graphdata_merged.m"];
 Export[NotebookDirectory[]<>"data/graphdata_merged_new.m",Join[mergedData,newData]]
 
 
+(* ::Section:: *)
+(*Plot triangle counts*)
+
+
 (* ::Subsection:: *)
 (*Plot empirical distribution of maximum degree*)
 
@@ -258,8 +271,16 @@ TableForm[histograms,TableHeadings->{taulabels,nlabels}]
 
 
 (* gdata[[ tau index, n index, run index , {ntau, #tris, ds, greedyTriangles} ]] *)
-successrates=Map[Length[#[[4]]]&,gdata,{3}];
+successrates=Map[{Length[#[[4]]],Length[#[[5]]]}&,gdata,{3}];
+successrates=Map[Transpose,successrates,{2}];
+successratesDelta=Map[Length[#[[5]]]-Length[#[[4]]]&,gdata,{3}];
 
 rateHistograms=Map[Histogram[#,{10},PlotRange->{{0,100},Automatic}]&,successrates,{2}];
 TableForm[rateHistograms,TableHeadings->{taulabels,nlabels}]
+
+rateHistograms=Map[Histogram[#,{10},PlotRange->{{-100,100},Automatic}]&,successratesDelta,{2}];
+TableForm[rateHistograms,TableHeadings->{taulabels,nlabels}]
 (*TableForm[Transpose[rateHistograms],TableHeadings->{nlabels,taulabels}]*)
+
+
+