diff --git a/triangle_analysis.m b/triangle_analysis.m
index d20b4e5fa65f4bbd6933972a84721872447805de..5cd064dd2563dbfddce90d6a3d9d2d2b5c66bdf6 100644
--- a/triangle_analysis.m
+++ b/triangle_analysis.m
@@ -210,12 +210,24 @@ fitList=Map[NonlinearModelFit[#[[2]],Exp[-(t-t0)/tmix]+c,{{tmix,1000},{t0,10000}
 (* Use fit["BestFitParameters"] to get parameters *)
 (* Use fit[t] to get fit value *)
 fitFuncsT=Map[#[t]&,fitList];
+tmixList=Map[tmix/.#["BestFitParameters"]&,fitList];
 
 
 timeplot1=ListPlot[coarseData,Joined->True,PlotRange->{0*minCount,maxCount},DataRange->{0,measureSkip*maxTime},PlotStyle->Opacity[0.5]];
 Show[timeplot1,Plot[fitFuncsT,{t,1,maxTime},PlotRange->All]]
 
 
+(* Log version of exponential fits *)
+fitAverages=Map[c/.#["BestFitParameters"]&,fitList];
+shiftedFitFuncsT=MapIndexed[#1[t]-fitAverages[[#2[[1]]]]&,fitList];
+shiftedCoarseData=MapIndexed[MovingAverage[#1[[2]],1000][[1;;-1;;skipPts]]-fitAverages[[#2[[1]]]]&,selectedData];
+
+
+(* Plot log version *)
+timeplot2=ListLogPlot[shiftedCoarseData[[1;;5]],Joined->True,PlotRange->{0*minCount+0.1,maxCount},DataRange->{0,measureSkip*maxTime},PlotStyle->Opacity[0.5]];
+Show[timeplot2,LogPlot[Evaluate[shiftedFitFuncsT[[1;;5]]],{t,1,maxTime},PlotRange->All,PlotStyle->Dotted]]
+
+
 (* ::Subsection:: *)
 (*Plot success rate over "time"*)