diff --git a/triangle_analysis.m b/triangle_analysis.m
index a2e2319b7f4f9b7f696ce84f1212bd4a7d75ec35..11db583ec5f2efcdd0999b4210afb90e48a2b940 100644
--- a/triangle_analysis.m
+++ b/triangle_analysis.m
@@ -71,7 +71,7 @@ Needs["ErrorBarPlots`"]
 (*Data import*)
 
 
-gsraw=Import[NotebookDirectory[]<>"data/graphdata_partial.m"];
+gsraw=Import[NotebookDirectory[]<>"data/graphdata_temp2.m"];
 (* gsraw=SortBy[gsraw,{#[[1,1]]&,#[[1,2]]&}]; (* Sort by n and then by tau. The {} forces a *stable* sort because otherwise Mathematica sorts also on triangle count and other things. *) *)
 
 
@@ -146,19 +146,47 @@ Show[ListPlot[avgAndProp,AxesOrigin->{0,0},AxesLabel->{"degree-sequence-property
 (*Plot triangle count over "time" in Markov chain instances*)
 
 
-numPlots=20;
-selectedData=gdata[[4,-1]][[-numPlots;;-1]];
+numPlots=10;
+selectedData=gdata[[1,-1]][[-numPlots;;-1]];
 measureSkip=1;
 minCount=Min[Map[Min[#[[2]]]&,selectedData]];
 maxCount=Max[Map[Max[#[[2]]]&,selectedData]];
 maxTime=Max[Map[Length[#[[2]]]&,selectedData]];
-skipPts=Max[1,Round[maxTime/200]]; (* Plotting every point is slow. Plot only once per `skipPts` timesteps *)
-coarseData=Map[#[[2,1;;-1;;skipPts]]&,selectedData];
+maxTime=30000;
+skipPts=Max[1,Round[maxTime/400]]; (* Plotting every point is slow. Plot only once per `skipPts` timesteps *)
+coarseData=Map[#[[2,1;;maxTime;;skipPts]]&,selectedData];
 labels=Map["{n,tau} = "<>ToString[#[[1]]]&,selectedData];
-ListPlot[coarseData,Joined->True,PlotRange->{minCount,maxCount},DataRange->{0,measureSkip*maxTime},PlotLegends->labels]
+ListPlot[coarseData,Joined->True,PlotRange->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime},PlotLegends->labels]
 (* Map[ListPlot[#,Joined->True,PlotRange\[Rule]{minCount,maxCount},DataRange\[Rule]{0,maxTime}]&,coarseData] *)
 
 
+(* ::Subsection:: *)
+(*Plot success rate over "time"*)
+
+
+numPlots=10;
+selectedData=gdata[[1,-1]][[-numPlots;;-1]];
+measureSkip=100;
+maxTime=Max[Map[Length[#[[4]]]&,selectedData]];
+maxTime=10000;
+coarseData=Map[#[[4,1;;maxTime/measureSkip]]&,selectedData];
+labels=Map["{n,tau} = "<>ToString[#[[1]]]&,selectedData];
+ListPlot[coarseData,Joined->True,PlotRange->{0,100},DataRange->{0,maxTime},PlotLegends->labels]
+(* Map[ListPlot[#,Joined->True,PlotRange\[Rule]{minCount,maxCount},DataRange\[Rule]{0,maxTime}]&,coarseData] *)
+
+
+(* ::Subsection:: *)
+(*Correlation of avgsuccess rate vs other things*)
+
+
+compare1=Map[{Mean[#[[4]]],Mean[#[[2]]]}&,gdata,{3}];
+(* { GCM1 rate, GCM2 rate, mixing time from ErdosGallai } *)
+
+
+scatterPlots=Map[ListPlot[#,PlotRange->{{0,100},All},PlotStyle->PointSize[Large]]&,compare1,{2}];
+TableForm[scatterPlots,TableHeadings->{taulabels,nlabels}]
+
+
 (* ::Subsection:: *)
 (*Compute 'mixing time'*)