Changeset - 3e67925ef7ec
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Tom Bannink - 8 years ago 2017-08-15 17:57:03
tom.bannink@cwi.nl
Add autocorrelation notebook
3 files changed with 67 insertions and 6 deletions:
plots/ccm_initialtris.pdf
bin+mod
triangle_autocorrelations.m
59
triangle_gcm_initial_analysis.m
8
6
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plots/ccm_initialtris.pdf
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triangle_autocorrelations.m
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new file 100644
 
(* ::Package:: *)
 

			




	
	
	
		
 
(* ::Section:: *)

			




	
	
	
		
 
(*Data import*)

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
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:

			




	
	
	
		
 
1: {n,tau}

			




	
	
	
		
 
2: #triangles time sequence

			




	
	
	
		
 
3: degree sequence

			




	
	
	
		
 
4: GCM1 starting triangle counts

			




	
	
	
		
 
5: GCM2 starting triangle counts

			




	
	
	
		
 
6: GCM1 time sequence

			




	
	
	
		
 
7: GCM2 time sequence

			




	
	
	
		
 
*)

			




	
	
	
		
 
nlabels=Map["n = "<>ToString[#]&,gdata[[1,All,1,1,1]]];

			




	
	
	
		
 
taulabels=Map["tau = "<>ToString[#]&,gdata[[All,1,1,1,2]]];

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
(* ::Section:: *)

			




	
	
	
		
 
(*Triangle counts autocorrelation*)

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
doAutoCorrelate[dataset_,maxlag_]:=Module[{avg,data2,result},

			




	
	
	
		
 
avg=Mean[dataset];

			




	
	
	
		
 
data2=dataset-avg;

			




	
	
	
		
 
result=ParallelTable[Mean[data2[[1+lag;;]]*data2[[;;-1-lag]]],{lag,0,maxlag}];

			




	
	
	
		
 
result=result/result[[1]]

			




	
	
	
		
 
]

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
doPeriodicAutocorrelate[dataset_,maxlag_]:=Module[{f,result},

			




	
	
	
		
 
f=Fourier[dataset-Mean[dataset]];

			




	
	
	
		
 
result=InverseFourier[f*Conjugate[f]];

			




	
	
	
		
 
result=result[[1;;Min[Floor[Length[result]/2],maxlag]]];

			




	
	
	
		
 
result=Chop[result];

			




	
	
	
		
 
result=result/result[[1]]

			




	
	
	
		
 
]

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
dataX=gdata[[1,1]][[3,2]];

			




	
	
	
		
 
ListPlot[dataX,Joined->True,PlotRange->All]

			




	
	
	
		
 
dataX=dataX[[15000;;50000]];

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
cor1=doAutoCorrelate[dataX,2000];

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
cor2=doPeriodicAutocorrelate[dataX,20000];

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
ListPlot[{cor1,cor2},Joined->True,PlotRange->{-0.5,1}]

			




        

    


    
    

    

        

                

                    triangle_gcm_initial_analysis.m
                

                

                  
                      
                        

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@@ -35,51 +35,53 @@ taulabels=Map["\[Tau] = "<>ToString[#]&,gdata[[All,1,1,1,2]]];

			




	
	
	
		
 
 (* Consider all runs *)

			




	
	
	
		
 
getIt[x_,avg_]:=If[x[[2]]>=5, x[[1]]/avg,0]

			




	
	
	
		
 
getAverage[run_]:=Module[{avg},

			




	
	
	
		
 
    avg=run[[2]];

			




	
	
	
		
 
    If[avg>0,

			




	
	
	
		
 
    { getIt[run[[3]],avg], getIt[run[[4]],avg] }

			




	
	
	
		
 
    , {3,3}]

			




	
	
	
		
 
]

			




	
	
	
		
 
getTotalStats[runs_]:=Transpose[Map[getAverage,runs]];

			




	
	
	
		
 
totalStats=Map[getTotalStats,gdata,{2}];

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
(* Yellow: GCM1 (take new highest everytime *)

			




	
	
	
		
 
(* Blue: GCM2 (finish highest first, more similar to EG) *)

			




	
	
	
		
 
(* Yellow: CCMu (take new highest everytime *)

			




	
	
	
		
 
(* Blue: CCMb (finish highest first, more similar to EG) *)

			




	
	
	
		
 
histogramsTotal=Map[Histogram[#,{0.05},PlotRange->{{-0.5,2},Automatic},ImageSize->300,AxesOrigin->{0,0}]&,totalStats,{2}];

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
TableForm[histogramsTotal,TableHeadings->{taulabels,nlabels}]

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
(* ::Subsubsection:: *)

			




	
	
	
		
 
(*Exporting plots*)

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
makeHistogram[datasets_,n_,tau_]:=Histogram[datasets,{0.05},"Probability",

			




	
	
	
		
 
PlotRange->{{0,1},{0,0.5}},

			




	
	
	
		
 
PlotRange->{{0,1.3},{0,0.5}},

			




	
	
	
		
 
ImageSize->300,AxesOrigin->{0,0},

			




	
	
	
		
 
AspectRatio->3/5,

			




	
	
	
		
 
Frame->True,

			




	
	
	
		
 
FrameLabel->{"fraction of average #triangles at CCM start","frequency"},

			




	
	
	
		
 
ChartLegends->Placed[{"CCMu   avg = "<>ToString[NumberForm[N[Mean[datasets[[1]]]],2]],"CCMb   avg = "<>ToString[NumberForm[N[Mean[datasets[[2]]]],2]]},Center],

			




	
	
	
		
 
(*LabelingFunction->(Placed[NumberForm[#,{2,3}],Above]&),*)

			




	
	
	
		
 
(*LabelingFunction\[Rule](Placed[If[#2[[2]]\[Equal]1,NumberForm[#1,{2,3}],""],Above]&),*)

			




	
	
	
		
 
PlotLabel->n<>", "<>tau];

			




	
	
	
		
 
histogramsTotal=MapIndexed[makeHistogram[#1,nlabels[[#2[[2]]]],taulabels[[#2[[1]]]]]&,totalStats,{2}];

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
tauIndices={1,5,9};

			




	
	
	
		
 
nIndices={3};

			




	
	
	
		
 
(* TableForm[histogramsTotal[[tauIndices,nIndices]],TableHeadings->{taulabels[[tauIndices]],nlabels[[nIndices]]}] *)

			




	
	
	
		
 
plotgrid1=GraphicsGrid[histogramsTotal[[tauIndices,nIndices]]]

			




	
	
	
		
 
nIndices={1};

			




	
	
	
		
 
(* TableForm[histogramsTotal[[tauIndices,nIndices]],TableHeadings->{taulabels[[tauIndices]],nlabels[[nIndices]]}]*)

			




	
	
	
		
 
plotgrid1=GraphicsGrid[histogramsTotal[[tauIndices,nIndices]],ImageSize->350,ItemAspectRatio->3/5]

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
Export[NotebookDirectory[]<>"plots/ccm_initialtris.pdf",plotgrid1]

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
(* ::Subsection:: *)

			




	
	
	
		
 
(*GCM1 vs GCM2 success rates*)

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
successrates=Map[{#[[3,2]]/100,#[[4,2]]/100}&,gdata,{3}];

			




	
	
	
		
 
successrates=Map[Transpose,successrates,{2}];

			




	
	
	
		
 
successratesDelta=Map[#[[3,2]]-#[[4,2]]&,gdata,{3}];

			




        

    



    


    



    



      

      





    
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