AENC/switchchain Changeset - c9a771afb4ab · Centrum Wiskunde & Informatica (CWI)

Changeset - c9a771afb4ab

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Tom Bannink - 8 years ago 2017-06-03 11:27:42
tom.bannink@cwi.nl

Merge branch 'master' of https://scm.cwi.nl/AC/switchchain

2 files changed with 38 insertions and 5 deletions:

plots/timeevol.pdf

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triangle_analysis.m

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plots/timeevol.pdf

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triangle_analysis.m

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@@ @@ -62,25 +62,25 @@ Needs["ErrorBarPlots`"] @@
 (*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%.*)
 (**)
 (*Success rate of GCM seems to be correlated with mixing time from Erdos-Gallai: higher success rate implies lower mixing time.  *)
 (**)
 (*Initial #triangles in both GCM1 and GCM2 is always below the average #triangles whereas Erdos-Gallai is usually many times higher than average.*)
 (* ::Section:: *)
 (*Data import*)
-gsraw=Import[NotebookDirectory[]<>"data/graphdata_temp2.m"];
+gsraw=Import[NotebookDirectory[]<>"data/graphdata_timeevol.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. *) *)
 gdata=GatherBy[gsraw,{#[[1,2]]&,#[[1,1]]&}];
 (* Data format: *)
 (* gdata[[ tau index, n index, run index , datatype index ]] *)
 (* datatype index:
 : {n,tau}
 : #triangles time sequence
 : degree sequence
 : GCM1 starting triangle counts
 : GCM2 starting triangle counts
 (* gdata[[ tau index, n index, run index , {ntau, #tris, ds} ]] *)
 avgAndProp=ParallelMap[{getProperty[#[[3]]],Mean[#[[2,1;;-1]]]}&,gdata[[2,2,1;;100]]];
 Show[ListPlot[avgAndProp,AxesOrigin->{0,0},AxesLabel->{"degree-sequence-property","<#triangles>"},AspectRatio->1],Plot[x,{x,1,1000}]]
 (* ::Subsection:: *)
 (*Plot triangle count over "time" in Markov chain instances*)
 numPlots=10;
 selectedData=gdata[[1,-1]][[-numPlots;;-1]];
 numPlots=20;
 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]];
 maxTime=30000;
 skipPts=Max[1,Round[maxTime/400]]; (* Plotting every point is slow. Plot only once per `skipPts` timesteps *)
 (* maxTime=30000; *)
 skipPts=Max[1,Round[maxTime/500]]; (* 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->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime},PlotLegends->labels]
 (* Map[ListPlot[#,Joined->True,PlotRange\[Rule]{minCount,maxCount},DataRange\[Rule]{0,maxTime}]&,coarseData] *)
 selectedData=gdata[[1,1]];
 measureSkip=1;
 minCount=Min[Map[Min[#[[2]]]&,selectedData]];
 maxCount=Max[Map[Max[#[[2]]]&,selectedData]];
 maxTime=Max[Map[Length[#[[2]]]&,selectedData]];
 maxTime=30000;
 skipPts=Max[1,Round[maxTime/100]]; (* 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];
 plot1=ListPlot[coarseData[[1;;5]],Joined->True,PlotRange->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime}]
 plot2=ListPlot[coarseData[[6;;10]],Joined->True,PlotRange->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime}]
 plot3=ListPlot[coarseData[[11;;15]],Joined->True,PlotRange->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime}]
 plot4=ListPlot[coarseData[[16;;20]],Joined->True,PlotRange->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime}]
 (* For export *)
 numPlots=20;
 selectedData=gdata[[2,1]][[-numPlots;;-1]];
 measureSkip=1;
 minCount=Min[Map[Min[#[[2]]]&,selectedData]];
 maxCount=Max[Map[Max[#[[2]]]&,selectedData]];
 maxTime=Max[Map[Length[#[[2]]]&,selectedData]];
 (* maxTime=30000; *)
 skipPts=Max[1,Round[maxTime/5000]]; (* 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];
 plotTimeEvol=ListPlot[coarseData,Joined->True,PlotRange->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime},Frame->True,FrameLabel->{"timesteps","number of triangles"},ImageSize->300]
 (* Map[ListPlot[#,Joined->True,PlotRange\[Rule]{minCount,maxCount},DataRange\[Rule]{0,maxTime}]&,coarseData] *)
 Export[NotebookDirectory[]<>"plots/timeevol.pdf",plotTimeEvol]
 (* ::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]

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