diff --git a/.gitignore b/.gitignore
index e443807..527a78f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,4 +16,5 @@ testdata
 vault-key
 .idea
 .DS_Store
-bin
\ No newline at end of file
+bin
+benchmark/results.html
\ No newline at end of file
diff --git a/README.md b/README.md
index d3dd4ba..59973da 100644
--- a/README.md
+++ b/README.md
@@ -55,7 +55,7 @@ As compared to `ansible-vault` (v2.9.11 on python v3.8.5), typical actions take
 | encrypt_string | 429 ms | **64 ms** |
 | encrypt + decrypt | 1,087 ms | **168 ms** |
 
-See [`./benchmark/results.html`](https://raw.githubusercontent.com/GoodwayGroup/gwvault/master/benchmark/results.html) for a detailed breakdown of the results.
+See [`./benchmark/results.html`](./benchmark/results.html) for a detailed breakdown of the results after running the benchmark.
 
 ## Built With
 
diff --git a/benchmark/results.html b/benchmark/results.html
deleted file mode 100644
index 3962e5c..0000000
--- a/benchmark/results.html
+++ /dev/null
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placeholder};plot.getCanvas=function(){return surface.element};plot.getPlotOffset=function(){return plotOffset};plot.width=function(){return plotWidth};plot.height=function(){return plotHeight};plot.offset=function(){var o=eventHolder.offset();o.left+=plotOffset.left;o.top+=plotOffset.top;return o};plot.getData=function(){return series};plot.getAxes=function(){var res={},i;$.each(xaxes.concat(yaxes),function(_,axis){if(axis)res[axis.direction+(axis.n!=1?axis.n:"")+"axis"]=axis});return res};plot.getXAxes=function(){return xaxes};plot.getYAxes=function(){return yaxes};plot.c2p=canvasToAxisCoords;plot.p2c=axisToCanvasCoords;plot.getOptions=function(){return options};plot.highlight=highlight;plot.unhighlight=unhighlight;plot.triggerRedrawOverlay=triggerRedrawOverlay;plot.pointOffset=function(point){return{left:parseInt(xaxes[axisNumber(point,"x")-1].p2c(+point.x)+plotOffset.left,10),top:parseInt(yaxes[axisNumber(point,"y")-1].p2c(+point.y)+plotOffset.top,10)}};plot.shutdown=shutdown;plot.destroy=function(){shutdown();placeholder.removeData("plot").empty();series=[];options=null;surface=null;overlay=null;eventHolder=null;ctx=null;octx=null;xaxes=[];yaxes=[];hooks=null;highlights=[];plot=null};plot.resize=function(){var width=placeholder.width(),height=placeholder.height();surface.resize(width,height);overlay.resize(width,height)};plot.hooks=hooks;initPlugins(plot);parseOptions(options_);setupCanvases();setData(data_);setupGrid();draw();bindEvents();function executeHooks(hook,args){args=[plot].concat(args);for(var i=0;i<hook.length;++i)hook[i].apply(this,args)}function initPlugins(){var classes={Canvas:Canvas};for(var i=0;i<plugins.length;++i){var p=plugins[i];p.init(plot,classes);if(p.options)$.extend(true,options,p.options)}}function parseOptions(opts){$.extend(true,options,opts);if(opts&&opts.colors){options.colors=opts.colors}if(options.xaxis.color==null)options.xaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.yaxis.color==null)options.yaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.xaxis.tickColor==null)options.xaxis.tickColor=options.grid.tickColor||options.xaxis.color;if(options.yaxis.tickColor==null)options.yaxis.tickColor=options.grid.tickColor||options.yaxis.color;if(options.grid.borderColor==null)options.grid.borderColor=options.grid.color;if(options.grid.tickColor==null)options.grid.tickColor=$.color.parse(options.grid.color).scale("a",.22).toString();var i,axisOptions,axisCount,fontSize=placeholder.css("font-size"),fontSizeDefault=fontSize?+fontSize.replace("px",""):13,fontDefaults={style:placeholder.css("font-style"),size:Math.round(.8*fontSizeDefault),variant:placeholder.css("font-variant"),weight:placeholder.css("font-weight"),family:placeholder.css("font-family")};axisCount=options.xaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.xaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.xaxis,axisOptions);options.xaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}axisCount=options.yaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.yaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.yaxis,axisOptions);options.yaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}if(options.xaxis.noTicks&&options.xaxis.ticks==null)options.xaxis.ticks=options.xaxis.noTicks;if(options.yaxis.noTicks&&options.yaxis.ticks==null)options.yaxis.ticks=options.yaxis.noTicks;if(options.x2axis){options.xaxes[1]=$.extend(true,{},options.xaxis,options.x2axis);options.xaxes[1].position="top";if(options.x2axis.min==null){options.xaxes[1].min=null}if(options.x2axis.max==null){options.xaxes[1].max=null}}if(options.y2axis){options.yaxes[1]=$.extend(true,{},options.yaxis,options.y2axis);options.yaxes[1].position="right";if(options.y2axis.min==null){options.yaxes[1].min=null}if(options.y2axis.max==null){options.yaxes[1].max=null}}if(options.grid.coloredAreas)options.grid.markings=options.grid.coloredAreas;if(options.grid.coloredAreasColor)options.grid.markingsColor=options.grid.coloredAreasColor;if(options.lines)$.extend(true,options.series.lines,options.lines);if(options.points)$.extend(true,options.series.points,options.points);if(options.bars)$.extend(true,options.series.bars,options.bars);if(options.shadowSize!=null)options.series.shadowSize=options.shadowSize;if(options.highlightColor!=null)options.series.highlightColor=options.highlightColor;for(i=0;i<options.xaxes.length;++i)getOrCreateAxis(xaxes,i+1).options=options.xaxes[i];for(i=0;i<options.yaxes.length;++i)getOrCreateAxis(yaxes,i+1).options=options.yaxes[i];for(var n in hooks)if(options.hooks[n]&&options.hooks[n].length)hooks[n]=hooks[n].concat(options.hooks[n]);executeHooks(hooks.processOptions,[options])}function setData(d){series=parseData(d);fillInSeriesOptions();processData()}function parseData(d){var res=[];for(var i=0;i<d.length;++i){var s=$.extend(true,{},options.series);if(d[i].data!=null){s.data=d[i].data;delete d[i].data;$.extend(true,s,d[i]);d[i].data=s.data}else s.data=d[i];res.push(s)}return res}function axisNumber(obj,coord){var a=obj[coord+"axis"];if(typeof a=="object")a=a.n;if(typeof a!="number")a=1;return a}function allAxes(){return $.grep(xaxes.concat(yaxes),function(a){return a})}function canvasToAxisCoords(pos){var res={},i,axis;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used)res["x"+axis.n]=axis.c2p(pos.left)}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used)res["y"+axis.n]=axis.c2p(pos.top)}if(res.x1!==undefined)res.x=res.x1;if(res.y1!==undefined)res.y=res.y1;return res}function axisToCanvasCoords(pos){var res={},i,axis,key;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used){key="x"+axis.n;if(pos[key]==null&&axis.n==1)key="x";if(pos[key]!=null){res.left=axis.p2c(pos[key]);break}}}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used){key="y"+axis.n;if(pos[key]==null&&axis.n==1)key="y";if(pos[key]!=null){res.top=axis.p2c(pos[key]);break}}}return res}function getOrCreateAxis(axes,number){if(!axes[number-1])axes[number-1]={n:number,direction:axes==xaxes?"x":"y",options:$.extend(true,{},axes==xaxes?options.xaxis:options.yaxis)};return axes[number-1]}function fillInSeriesOptions(){var neededColors=series.length,maxIndex=-1,i;for(i=0;i<series.length;++i){var sc=series[i].color;if(sc!=null){neededColors--;if(typeof sc=="number"&&sc>maxIndex){maxIndex=sc}}}if(neededColors<=maxIndex){neededColors=maxIndex+1}var c,colors=[],colorPool=options.colors,colorPoolSize=colorPool.length,variation=0;for(i=0;i<neededColors;i++){c=$.color.parse(colorPool[i%colorPoolSize]||"#666");if(i%colorPoolSize==0&&i){if(variation>=0){if(variation<.5){variation=-variation-.2}else variation=0}else variation=-variation}colors[i]=c.scale("rgb",1+variation)}var colori=0,s;for(i=0;i<series.length;++i){s=series[i];if(s.color==null){s.color=colors[colori].toString();++colori}else if(typeof s.color=="number")s.color=colors[s.color].toString();if(s.lines.show==null){var v,show=true;for(v in s)if(s[v]&&s[v].show){show=false;break}if(show)s.lines.show=true}if(s.lines.zero==null){s.lines.zero=!!s.lines.fill}s.xaxis=getOrCreateAxis(xaxes,axisNumber(s,"x"));s.yaxis=getOrCreateAxis(yaxes,axisNumber(s,"y"))}}function processData(){var topSentry=Number.POSITIVE_INFINITY,bottomSentry=Number.NEGATIVE_INFINITY,fakeInfinity=Number.MAX_VALUE,i,j,k,m,length,s,points,ps,x,y,axis,val,f,p,data,format;function 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format[format.length-1].y;format[format.length-1].x=true}}s.datapoints.format=format}if(s.datapoints.pointsize!=null)continue;s.datapoints.pointsize=format.length;ps=s.datapoints.pointsize;points=s.datapoints.points;var insertSteps=s.lines.show&&s.lines.steps;s.xaxis.used=s.yaxis.used=true;for(j=k=0;j<data.length;++j,k+=ps){p=data[j];var nullify=p==null;if(!nullify){for(m=0;m<ps;++m){val=p[m];f=format[m];if(f){if(f.number&&val!=null){val=+val;if(isNaN(val))val=null;else if(val==Infinity)val=fakeInfinity;else if(val==-Infinity)val=-fakeInfinity}if(val==null){if(f.required)nullify=true;if(f.defaultValue!=null)val=f.defaultValue}}points[k+m]=val}}if(nullify){for(m=0;m<ps;++m){val=points[k+m];if(val!=null){f=format[m];if(f.autoscale!==false){if(f.x){updateAxis(s.xaxis,val,val)}if(f.y){updateAxis(s.yaxis,val,val)}}}points[k+m]=null}}else{if(insertSteps&&k>0&&points[k-ps]!=null&&points[k-ps]!=points[k]&&points[k-ps+1]!=points[k+1]){for(m=0;m<ps;++m)points[k+ps+m]=points[k+m];points[k+1]=points[k-ps+1];k+=ps}}}}for(i=0;i<series.length;++i){s=series[i];executeHooks(hooks.processDatapoints,[s,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];points=s.datapoints.points;ps=s.datapoints.pointsize;format=s.datapoints.format;var xmin=topSentry,ymin=topSentry,xmax=bottomSentry,ymax=bottomSentry;for(j=0;j<points.length;j+=ps){if(points[j]==null)continue;for(m=0;m<ps;++m){val=points[j+m];f=format[m];if(!f||f.autoscale===false||val==fakeInfinity||val==-fakeInfinity)continue;if(f.x){if(val<xmin)xmin=val;if(val>xmax)xmax=val}if(f.y){if(val<ymin)ymin=val;if(val>ymax)ymax=val}}}if(s.bars.show){var delta;switch(s.bars.align){case"left":delta=0;break;case"right":delta=-s.bars.barWidth;break;default:delta=-s.bars.barWidth/2}if(s.bars.horizontal){ymin+=delta;ymax+=delta+s.bars.barWidth}else{xmin+=delta;xmax+=delta+s.bars.barWidth}}updateAxis(s.xaxis,xmin,xmax);updateAxis(s.yaxis,ymin,ymax)}$.each(allAxes(),function(_,axis){if(axis.datamin==topSentry)axis.datamin=null;if(axis.datamax==bottomSentry)axis.datamax=null})}function 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setTransformationHelpers(axis){function identity(x){return x}var s,m,t=axis.options.transform||identity,it=axis.options.inverseTransform;if(axis.direction=="x"){s=axis.scale=plotWidth/Math.abs(t(axis.max)-t(axis.min));m=Math.min(t(axis.max),t(axis.min))}else{s=axis.scale=plotHeight/Math.abs(t(axis.max)-t(axis.min));s=-s;m=Math.max(t(axis.max),t(axis.min))}if(t==identity)axis.p2c=function(p){return(p-m)*s};else axis.p2c=function(p){return(t(p)-m)*s};if(!it)axis.c2p=function(c){return m+c/s};else axis.c2p=function(c){return it(m+c/s)}}function measureTickLabels(axis){var opts=axis.options,ticks=axis.ticks||[],labelWidth=opts.labelWidth||0,labelHeight=opts.labelHeight||0,maxWidth=labelWidth||(axis.direction=="x"?Math.floor(surface.width/(ticks.length||1)):null),legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=opts.font||"flot-tick-label tickLabel";for(var i=0;i<ticks.length;++i){var t=ticks[i];if(!t.label)continue;var info=surface.getTextInfo(layer,t.label,font,null,maxWidth);labelWidth=Math.max(labelWidth,info.width);labelHeight=Math.max(labelHeight,info.height)}axis.labelWidth=opts.labelWidth||labelWidth;axis.labelHeight=opts.labelHeight||labelHeight}function allocateAxisBoxFirstPhase(axis){var lw=axis.labelWidth,lh=axis.labelHeight,pos=axis.options.position,isXAxis=axis.direction==="x",tickLength=axis.options.tickLength,axisMargin=options.grid.axisMargin,padding=options.grid.labelMargin,innermost=true,outermost=true,first=true,found=false;$.each(isXAxis?xaxes:yaxes,function(i,a){if(a&&(a.show||a.reserveSpace)){if(a===axis){found=true}else if(a.options.position===pos){if(found){outermost=false}else{innermost=false}}if(!found){first=false}}});if(outermost){axisMargin=0}if(tickLength==null){tickLength=first?"full":5}if(!isNaN(+tickLength))padding+=+tickLength;if(isXAxis){lh+=padding;if(pos=="bottom"){plotOffset.bottom+=lh+axisMargin;axis.box={top:surface.height-plotOffset.bottom,height:lh}}else{axis.box={top:plotOffset.top+axisMargin,height:lh};plotOffset.top+=lh+axisMargin}}else{lw+=padding;if(pos=="left"){axis.box={left:plotOffset.left+axisMargin,width:lw};plotOffset.left+=lw+axisMargin}else{plotOffset.right+=lw+axisMargin;axis.box={left:surface.width-plotOffset.right,width:lw}}}axis.position=pos;axis.tickLength=tickLength;axis.box.padding=padding;axis.innermost=innermost}function allocateAxisBoxSecondPhase(axis){if(axis.direction=="x"){axis.box.left=plotOffset.left-axis.labelWidth/2;axis.box.width=surface.width-plotOffset.left-plotOffset.right+axis.labelWidth}else{axis.box.top=plotOffset.top-axis.labelHeight/2;axis.box.height=surface.height-plotOffset.bottom-plotOffset.top+axis.labelHeight}}function adjustLayoutForThingsStickingOut(){var minMargin=options.grid.minBorderMargin,axis,i;if(minMargin==null){minMargin=0;for(i=0;i<series.length;++i)minMargin=Math.max(minMargin,2*(series[i].points.radius+series[i].points.lineWidth/2))}var margins={left:minMargin,right:minMargin,top:minMargin,bottom:minMargin};$.each(allAxes(),function(_,axis){if(axis.reserveSpace&&axis.ticks&&axis.ticks.length){if(axis.direction==="x"){margins.left=Math.max(margins.left,axis.labelWidth/2);margins.right=Math.max(margins.right,axis.labelWidth/2)}else{margins.bottom=Math.max(margins.bottom,axis.labelHeight/2);margins.top=Math.max(margins.top,axis.labelHeight/2)}}});plotOffset.left=Math.ceil(Math.max(margins.left,plotOffset.left));plotOffset.right=Math.ceil(Math.max(margins.right,plotOffset.right));plotOffset.top=Math.ceil(Math.max(margins.top,plotOffset.top));plotOffset.bottom=Math.ceil(Math.max(margins.bottom,plotOffset.bottom))}function setupGrid(){var i,axes=allAxes(),showGrid=options.grid.show;for(var a in plotOffset){var margin=options.grid.margin||0;plotOffset[a]=typeof margin=="number"?margin:margin[a]||0}executeHooks(hooks.processOffset,[plotOffset]);for(var a in plotOffset){if(typeof options.grid.borderWidth=="object"){plotOffset[a]+=showGrid?options.grid.borderWidth[a]:0}else{plotOffset[a]+=showGrid?options.grid.borderWidth:0}}$.each(axes,function(_,axis){var axisOpts=axis.options;axis.show=axisOpts.show==null?axis.used:axisOpts.show;axis.reserveSpace=axisOpts.reserveSpace==null?axis.show:axisOpts.reserveSpace;setRange(axis)});if(showGrid){var allocatedAxes=$.grep(axes,function(axis){return axis.show||axis.reserveSpace});$.each(allocatedAxes,function(_,axis){setupTickGeneration(axis);setTicks(axis);snapRangeToTicks(axis,axis.ticks);measureTickLabels(axis)});for(i=allocatedAxes.length-1;i>=0;--i)allocateAxisBoxFirstPhase(allocatedAxes[i]);adjustLayoutForThingsStickingOut();$.each(allocatedAxes,function(_,axis){allocateAxisBoxSecondPhase(axis)})}plotWidth=surface.width-plotOffset.left-plotOffset.right;plotHeight=surface.height-plotOffset.bottom-plotOffset.top;$.each(axes,function(_,axis){setTransformationHelpers(axis)});if(showGrid){drawAxisLabels()}insertLegend()}function setRange(axis){var opts=axis.options,min=+(opts.min!=null?opts.min:axis.datamin),max=+(opts.max!=null?opts.max:axis.datamax),delta=max-min;if(delta==0){var widen=max==0?1:.01;if(opts.min==null)min-=widen;if(opts.max==null||opts.min!=null)max+=widen}else{var margin=opts.autoscaleMargin;if(margin!=null){if(opts.min==null){min-=delta*margin;if(min<0&&axis.datamin!=null&&axis.datamin>=0)min=0}if(opts.max==null){max+=delta*margin;if(max>0&&axis.datamax!=null&&axis.datamax<=0)max=0}}}axis.min=min;axis.max=max}function setupTickGeneration(axis){var opts=axis.options;var noTicks;if(typeof opts.ticks=="number"&&opts.ticks>0)noTicks=opts.ticks;else noTicks=.3*Math.sqrt(axis.direction=="x"?surface.width:surface.height);var delta=(axis.max-axis.min)/noTicks,dec=-Math.floor(Math.log(delta)/Math.LN10),maxDec=opts.tickDecimals;if(maxDec!=null&&dec>maxDec){dec=maxDec}var magn=Math.pow(10,-dec),norm=delta/magn,size;if(norm<1.5){size=1}else if(norm<3){size=2;if(norm>2.25&&(maxDec==null||dec+1<=maxDec)){size=2.5;++dec}}else if(norm<7.5){size=5}else{size=10}size*=magn;if(opts.minTickSize!=null&&size<opts.minTickSize){size=opts.minTickSize}axis.delta=delta;axis.tickDecimals=Math.max(0,maxDec!=null?maxDec:dec);axis.tickSize=opts.tickSize||size;if(opts.mode=="time"&&!axis.tickGenerator){throw new Error("Time mode requires the flot.time plugin.")}if(!axis.tickGenerator){axis.tickGenerator=function(axis){var ticks=[],start=floorInBase(axis.min,axis.tickSize),i=0,v=Number.NaN,prev;do{prev=v;v=start+i*axis.tickSize;ticks.push(v);++i}while(v<axis.max&&v!=prev);return ticks};axis.tickFormatter=function(value,axis){var factor=axis.tickDecimals?Math.pow(10,axis.tickDecimals):1;var formatted=""+Math.round(value*factor)/factor;if(axis.tickDecimals!=null){var decimal=formatted.indexOf(".");var precision=decimal==-1?0:formatted.length-decimal-1;if(precision<axis.tickDecimals){return(precision?formatted:formatted+".")+(""+factor).substr(1,axis.tickDecimals-precision)}}return formatted}}if($.isFunction(opts.tickFormatter))axis.tickFormatter=function(v,axis){return""+opts.tickFormatter(v,axis)};if(opts.alignTicksWithAxis!=null){var otherAxis=(axis.direction=="x"?xaxes:yaxes)[opts.alignTicksWithAxis-1];if(otherAxis&&otherAxis.used&&otherAxis!=axis){var niceTicks=axis.tickGenerator(axis);if(niceTicks.length>0){if(opts.min==null)axis.min=Math.min(axis.min,niceTicks[0]);if(opts.max==null&&niceTicks.length>1)axis.max=Math.max(axis.max,niceTicks[niceTicks.length-1])}axis.tickGenerator=function(axis){var ticks=[],v,i;for(i=0;i<otherAxis.ticks.length;++i){v=(otherAxis.ticks[i].v-otherAxis.min)/(otherAxis.max-otherAxis.min);v=axis.min+v*(axis.max-axis.min);ticks.push(v)}return ticks};if(!axis.mode&&opts.tickDecimals==null){var extraDec=Math.max(0,-Math.floor(Math.log(axis.delta)/Math.LN10)+1),ts=axis.tickGenerator(axis);if(!(ts.length>1&&/\..*0$/.test((ts[1]-ts[0]).toFixed(extraDec))))axis.tickDecimals=extraDec}}}}function setTicks(axis){var oticks=axis.options.ticks,ticks=[];if(oticks==null||typeof oticks=="number"&&oticks>0)ticks=axis.tickGenerator(axis);else if(oticks){if($.isFunction(oticks))ticks=oticks(axis);else ticks=oticks}var i,v;axis.ticks=[];for(i=0;i<ticks.length;++i){var label=null;var t=ticks[i];if(typeof t=="object"){v=+t[0];if(t.length>1)label=t[1]}else v=+t;if(label==null)label=axis.tickFormatter(v,axis);if(!isNaN(v))axis.ticks.push({v:v,label:label})}}function snapRangeToTicks(axis,ticks){if(axis.options.autoscaleMargin&&ticks.length>0){if(axis.options.min==null)axis.min=Math.min(axis.min,ticks[0].v);if(axis.options.max==null&&ticks.length>1)axis.max=Math.max(axis.max,ticks[ticks.length-1].v)}}function draw(){surface.clear();executeHooks(hooks.drawBackground,[ctx]);var grid=options.grid;if(grid.show&&grid.backgroundColor)drawBackground();if(grid.show&&!grid.aboveData){drawGrid()}for(var i=0;i<series.length;++i){executeHooks(hooks.drawSeries,[ctx,series[i]]);drawSeries(series[i])}executeHooks(hooks.draw,[ctx]);if(grid.show&&grid.aboveData){drawGrid()}surface.render();triggerRedrawOverlay()}function extractRange(ranges,coord){var axis,from,to,key,axes=allAxes();for(var i=0;i<axes.length;++i){axis=axes[i];if(axis.direction==coord){key=coord+axis.n+"axis";if(!ranges[key]&&axis.n==1)key=coord+"axis";if(ranges[key]){from=ranges[key].from;to=ranges[key].to;break}}}if(!ranges[key]){axis=coord=="x"?xaxes[0]:yaxes[0];from=ranges[coord+"1"];to=ranges[coord+"2"]}if(from!=null&&to!=null&&from>to){var tmp=from;from=to;to=tmp}return{from:from,to:to,axis:axis}}function drawBackground(){ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.fillStyle=getColorOrGradient(options.grid.backgroundColor,plotHeight,0,"rgba(255, 255, 255, 0)");ctx.fillRect(0,0,plotWidth,plotHeight);ctx.restore()}function drawGrid(){var i,axes,bw,bc;ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var markings=options.grid.markings;if(markings){if($.isFunction(markings)){axes=plot.getAxes();axes.xmin=axes.xaxis.min;axes.xmax=axes.xaxis.max;axes.ymin=axes.yaxis.min;axes.ymax=axes.yaxis.max;markings=markings(axes)}for(i=0;i<markings.length;++i){var m=markings[i],xrange=extractRange(m,"x"),yrange=extractRange(m,"y");if(xrange.from==null)xrange.from=xrange.axis.min;if(xrange.to==null)xrange.to=xrange.axis.max;
-if(yrange.from==null)yrange.from=yrange.axis.min;if(yrange.to==null)yrange.to=yrange.axis.max;if(xrange.to<xrange.axis.min||xrange.from>xrange.axis.max||yrange.to<yrange.axis.min||yrange.from>yrange.axis.max)continue;xrange.from=Math.max(xrange.from,xrange.axis.min);xrange.to=Math.min(xrange.to,xrange.axis.max);yrange.from=Math.max(yrange.from,yrange.axis.min);yrange.to=Math.min(yrange.to,yrange.axis.max);var xequal=xrange.from===xrange.to,yequal=yrange.from===yrange.to;if(xequal&&yequal){continue}xrange.from=Math.floor(xrange.axis.p2c(xrange.from));xrange.to=Math.floor(xrange.axis.p2c(xrange.to));yrange.from=Math.floor(yrange.axis.p2c(yrange.from));yrange.to=Math.floor(yrange.axis.p2c(yrange.to));if(xequal||yequal){var lineWidth=m.lineWidth||options.grid.markingsLineWidth,subPixel=lineWidth%2?.5:0;ctx.beginPath();ctx.strokeStyle=m.color||options.grid.markingsColor;ctx.lineWidth=lineWidth;if(xequal){ctx.moveTo(xrange.to+subPixel,yrange.from);ctx.lineTo(xrange.to+subPixel,yrange.to)}else{ctx.moveTo(xrange.from,yrange.to+subPixel);ctx.lineTo(xrange.to,yrange.to+subPixel)}ctx.stroke()}else{ctx.fillStyle=m.color||options.grid.markingsColor;ctx.fillRect(xrange.from,yrange.to,xrange.to-xrange.from,yrange.from-yrange.to)}}}axes=allAxes();bw=options.grid.borderWidth;for(var j=0;j<axes.length;++j){var axis=axes[j],box=axis.box,t=axis.tickLength,x,y,xoff,yoff;if(!axis.show||axis.ticks.length==0)continue;ctx.lineWidth=1;if(axis.direction=="x"){x=0;if(t=="full")y=axis.position=="top"?0:plotHeight;else y=box.top-plotOffset.top+(axis.position=="top"?box.height:0)}else{y=0;if(t=="full")x=axis.position=="left"?0:plotWidth;else x=box.left-plotOffset.left+(axis.position=="left"?box.width:0)}if(!axis.innermost){ctx.strokeStyle=axis.options.color;ctx.beginPath();xoff=yoff=0;if(axis.direction=="x")xoff=plotWidth+1;else yoff=plotHeight+1;if(ctx.lineWidth==1){if(axis.direction=="x"){y=Math.floor(y)+.5}else{x=Math.floor(x)+.5}}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff);ctx.stroke()}ctx.strokeStyle=axis.options.tickColor;ctx.beginPath();for(i=0;i<axis.ticks.length;++i){var v=axis.ticks[i].v;xoff=yoff=0;if(isNaN(v)||v<axis.min||v>axis.max||t=="full"&&(typeof bw=="object"&&bw[axis.position]>0||bw>0)&&(v==axis.min||v==axis.max))continue;if(axis.direction=="x"){x=axis.p2c(v);yoff=t=="full"?-plotHeight:t;if(axis.position=="top")yoff=-yoff}else{y=axis.p2c(v);xoff=t=="full"?-plotWidth:t;if(axis.position=="left")xoff=-xoff}if(ctx.lineWidth==1){if(axis.direction=="x")x=Math.floor(x)+.5;else y=Math.floor(y)+.5}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff)}ctx.stroke()}if(bw){bc=options.grid.borderColor;if(typeof bw=="object"||typeof bc=="object"){if(typeof bw!=="object"){bw={top:bw,right:bw,bottom:bw,left:bw}}if(typeof bc!=="object"){bc={top:bc,right:bc,bottom:bc,left:bc}}if(bw.top>0){ctx.strokeStyle=bc.top;ctx.lineWidth=bw.top;ctx.beginPath();ctx.moveTo(0-bw.left,0-bw.top/2);ctx.lineTo(plotWidth,0-bw.top/2);ctx.stroke()}if(bw.right>0){ctx.strokeStyle=bc.right;ctx.lineWidth=bw.right;ctx.beginPath();ctx.moveTo(plotWidth+bw.right/2,0-bw.top);ctx.lineTo(plotWidth+bw.right/2,plotHeight);ctx.stroke()}if(bw.bottom>0){ctx.strokeStyle=bc.bottom;ctx.lineWidth=bw.bottom;ctx.beginPath();ctx.moveTo(plotWidth+bw.right,plotHeight+bw.bottom/2);ctx.lineTo(0,plotHeight+bw.bottom/2);ctx.stroke()}if(bw.left>0){ctx.strokeStyle=bc.left;ctx.lineWidth=bw.left;ctx.beginPath();ctx.moveTo(0-bw.left/2,plotHeight+bw.bottom);ctx.lineTo(0-bw.left/2,0);ctx.stroke()}}else{ctx.lineWidth=bw;ctx.strokeStyle=options.grid.borderColor;ctx.strokeRect(-bw/2,-bw/2,plotWidth+bw,plotHeight+bw)}}ctx.restore()}function drawAxisLabels(){$.each(allAxes(),function(_,axis){var box=axis.box,legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=axis.options.font||"flot-tick-label tickLabel",tick,x,y,halign,valign;surface.removeText(layer);if(!axis.show||axis.ticks.length==0)return;for(var i=0;i<axis.ticks.length;++i){tick=axis.ticks[i];if(!tick.label||tick.v<axis.min||tick.v>axis.max)continue;if(axis.direction=="x"){halign="center";x=plotOffset.left+axis.p2c(tick.v);if(axis.position=="bottom"){y=box.top+box.padding}else{y=box.top+box.height-box.padding;valign="bottom"}}else{valign="middle";y=plotOffset.top+axis.p2c(tick.v);if(axis.position=="left"){x=box.left+box.width-box.padding;halign="right"}else{x=box.left+box.padding}}surface.addText(layer,x,y,tick.label,font,null,null,halign,valign)}})}function drawSeries(series){if(series.lines.show)drawSeriesLines(series);if(series.bars.show)drawSeriesBars(series);if(series.points.show)drawSeriesPoints(series)}function drawSeriesLines(series){function plotLine(datapoints,xoffset,yoffset,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,prevx=null,prevy=null;ctx.beginPath();for(var i=ps;i<points.length;i+=ps){var x1=points[i-ps],y1=points[i-ps+1],x2=points[i],y2=points[i+1];if(x1==null||x2==null)continue;if(y1<=y2&&y1<axisy.min){if(y2<axisy.min)continue;x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min){if(y1<axisy.min)continue;x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max){if(y2>axisy.max)continue;x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max){if(y1>axisy.max)continue;x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(x1!=prevx||y1!=prevy)ctx.moveTo(axisx.p2c(x1)+xoffset,axisy.p2c(y1)+yoffset);prevx=x2;prevy=y2;ctx.lineTo(axisx.p2c(x2)+xoffset,axisy.p2c(y2)+yoffset)}ctx.stroke()}function plotLineArea(datapoints,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,bottom=Math.min(Math.max(0,axisy.min),axisy.max),i=0,top,areaOpen=false,ypos=1,segmentStart=0,segmentEnd=0;while(true){if(ps>0&&i>points.length+ps)break;i+=ps;var x1=points[i-ps],y1=points[i-ps+ypos],x2=points[i],y2=points[i+ypos];if(areaOpen){if(ps>0&&x1!=null&&x2==null){segmentEnd=i;ps=-ps;ypos=2;continue}if(ps<0&&i==segmentStart+ps){ctx.fill();areaOpen=false;ps=-ps;ypos=1;i=segmentStart=segmentEnd+ps;continue}}if(x1==null||x2==null)continue;if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(!areaOpen){ctx.beginPath();ctx.moveTo(axisx.p2c(x1),axisy.p2c(bottom));areaOpen=true}if(y1>=axisy.max&&y2>=axisy.max){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.max));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.max));continue}else if(y1<=axisy.min&&y2<=axisy.min){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.min));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.min));continue}var x1old=x1,x2old=x2;if(y1<=y2&&y1<axisy.min&&y2>=axisy.min){x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min&&y1>=axisy.min){x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max&&y2<=axisy.max){x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max&&y1<=axisy.max){x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1!=x1old){ctx.lineTo(axisx.p2c(x1old),axisy.p2c(y1))}ctx.lineTo(axisx.p2c(x1),axisy.p2c(y1));ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));if(x2!=x2old){ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));ctx.lineTo(axisx.p2c(x2old),axisy.p2c(y2))}}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineJoin="round";var lw=series.lines.lineWidth,sw=series.shadowSize;if(lw>0&&sw>0){ctx.lineWidth=sw;ctx.strokeStyle="rgba(0,0,0,0.1)";var angle=Math.PI/18;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/2),Math.cos(angle)*(lw/2+sw/2),series.xaxis,series.yaxis);ctx.lineWidth=sw/2;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/4),Math.cos(angle)*(lw/2+sw/4),series.xaxis,series.yaxis)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;var fillStyle=getFillStyle(series.lines,series.color,0,plotHeight);if(fillStyle){ctx.fillStyle=fillStyle;plotLineArea(series.datapoints,series.xaxis,series.yaxis)}if(lw>0)plotLine(series.datapoints,0,0,series.xaxis,series.yaxis);ctx.restore()}function drawSeriesPoints(series){function plotPoints(datapoints,radius,fillStyle,offset,shadow,axisx,axisy,symbol){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){var x=points[i],y=points[i+1];if(x==null||x<axisx.min||x>axisx.max||y<axisy.min||y>axisy.max)continue;ctx.beginPath();x=axisx.p2c(x);y=axisy.p2c(y)+offset;if(symbol=="circle")ctx.arc(x,y,radius,0,shadow?Math.PI:Math.PI*2,false);else symbol(ctx,x,y,radius,shadow);ctx.closePath();if(fillStyle){ctx.fillStyle=fillStyle;ctx.fill()}ctx.stroke()}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var lw=series.points.lineWidth,sw=series.shadowSize,radius=series.points.radius,symbol=series.points.symbol;if(lw==0)lw=1e-4;if(lw>0&&sw>0){var w=sw/2;ctx.lineWidth=w;ctx.strokeStyle="rgba(0,0,0,0.1)";plotPoints(series.datapoints,radius,null,w+w/2,true,series.xaxis,series.yaxis,symbol);ctx.strokeStyle="rgba(0,0,0,0.2)";plotPoints(series.datapoints,radius,null,w/2,true,series.xaxis,series.yaxis,symbol)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;plotPoints(series.datapoints,radius,getFillStyle(series.points,series.color),0,false,series.xaxis,series.yaxis,symbol);ctx.restore()}function drawBar(x,y,b,barLeft,barRight,fillStyleCallback,axisx,axisy,c,horizontal,lineWidth){var left,right,bottom,top,drawLeft,drawRight,drawTop,drawBottom,tmp;if(horizontal){drawBottom=drawRight=drawTop=true;drawLeft=false;left=b;right=x;top=y+barLeft;bottom=y+barRight;if(right<left){tmp=right;right=left;left=tmp;drawLeft=true;drawRight=false}}else{drawLeft=drawRight=drawTop=true;drawBottom=false;left=x+barLeft;right=x+barRight;bottom=b;top=y;if(top<bottom){tmp=top;top=bottom;bottom=tmp;drawBottom=true;drawTop=false}}if(right<axisx.min||left>axisx.max||top<axisy.min||bottom>axisy.max)return;if(left<axisx.min){left=axisx.min;drawLeft=false}if(right>axisx.max){right=axisx.max;drawRight=false}if(bottom<axisy.min){bottom=axisy.min;drawBottom=false}if(top>axisy.max){top=axisy.max;drawTop=false}left=axisx.p2c(left);bottom=axisy.p2c(bottom);right=axisx.p2c(right);top=axisy.p2c(top);if(fillStyleCallback){c.fillStyle=fillStyleCallback(bottom,top);c.fillRect(left,top,right-left,bottom-top)}if(lineWidth>0&&(drawLeft||drawRight||drawTop||drawBottom)){c.beginPath();c.moveTo(left,bottom);if(drawLeft)c.lineTo(left,top);else c.moveTo(left,top);if(drawTop)c.lineTo(right,top);else c.moveTo(right,top);if(drawRight)c.lineTo(right,bottom);else c.moveTo(right,bottom);if(drawBottom)c.lineTo(left,bottom);else c.moveTo(left,bottom);c.stroke()}}function drawSeriesBars(series){function plotBars(datapoints,barLeft,barRight,fillStyleCallback,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){if(points[i]==null)continue;drawBar(points[i],points[i+1],points[i+2],barLeft,barRight,fillStyleCallback,axisx,axisy,ctx,series.bars.horizontal,series.bars.lineWidth)}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineWidth=series.bars.lineWidth;ctx.strokeStyle=series.color;var barLeft;switch(series.bars.align){case"left":barLeft=0;break;case"right":barLeft=-series.bars.barWidth;break;default:barLeft=-series.bars.barWidth/2}var fillStyleCallback=series.bars.fill?function(bottom,top){return 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- </head>
-    <body>
-     <div id="wrap">
-      <div id="main" class="body">
-    <h1>criterion performance measurements</h1>
-
-<h2>overview</h2>
-
-<p><a href="#grokularation">want to understand this report?</a></p>
-
-<div id="overview" class="ovchart" style="width:900px;height:100px;"></div>
-
-<h2><a name="b0">bench/./ansible-vault-encrypt.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde0" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time0" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle0" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb0">xxx</span></td>
-    <td><span class="olstimept0">xxx</span></td>
-    <td><span class="confinterval olstimeub0">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb0">xxx</span></td>
-    <td><span class="olsr2pt0">xxx</span></td>
-    <td><span class="confinterval olsr2ub0">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">0.4847128241570614</span></td>
-    <td><span class="time">0.4908948086617951</span></td>
-    <td><span class="confinterval citime">0.5109107636187348</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">4.176360362081773e-3</span></td>
-    <td><span class="time">1.3673801077204834e-2</span></td>
-    <td><span class="confinterval citime">2.0399166016711115e-2</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have moderate
-     (<span class="percent">0.12244897959183673</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b1">bench/./gwvault-encrypt.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde1" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time1" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle1" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb1">xxx</span></td>
-    <td><span class="olstimept1">xxx</span></td>
-    <td><span class="confinterval olstimeub1">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb1">xxx</span></td>
-    <td><span class="olsr2pt1">xxx</span></td>
-    <td><span class="confinterval olsr2ub1">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">8.799614949608082e-2</span></td>
-    <td><span class="time">8.8629525776332e-2</span></td>
-    <td><span class="confinterval citime">8.900730617232591e-2</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">5.807767007341921e-4</span></td>
-    <td><span class="time">9.972547118101713e-4</span></td>
-    <td><span class="confinterval citime">1.5795410789369389e-3</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have slight
-     (<span class="percent">5.536332179930795e-2</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b2">bench/./ansible-vault-decrypt.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde2" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time2" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle2" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb2">xxx</span></td>
-    <td><span class="olstimept2">xxx</span></td>
-    <td><span class="confinterval olstimeub2">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb2">xxx</span></td>
-    <td><span class="olsr2pt2">xxx</span></td>
-    <td><span class="confinterval olsr2ub2">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">0.47682691878108013</span></td>
-    <td><span class="time">0.506753976247042</span></td>
-    <td><span class="confinterval citime">0.5443583530510778</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">3.818451484421398e-2</span></td>
-    <td><span class="time">5.0367134962777595e-2</span></td>
-    <td><span class="confinterval citime">5.789626276543197e-2</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have moderate
-     (<span class="percent">0.2672689853230438</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b3">bench/./gwvault-decrypt.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde3" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time3" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle3" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb3">xxx</span></td>
-    <td><span class="olstimept3">xxx</span></td>
-    <td><span class="confinterval olstimeub3">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb3">xxx</span></td>
-    <td><span class="olsr2pt3">xxx</span></td>
-    <td><span class="confinterval olsr2ub3">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">9.506872849859133e-2</span></td>
-    <td><span class="time">9.602444657299489e-2</span></td>
-    <td><span class="confinterval citime">9.696212148375674e-2</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">1.615210336380169e-3</span></td>
-    <td><span class="time">2.114096929002399e-3</span></td>
-    <td><span class="confinterval citime">2.8168711478317784e-3</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have slight
-     (<span class="percent">5.53633217993079e-2</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b4">bench/./ansible-vault-string.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde4" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time4" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle4" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb4">xxx</span></td>
-    <td><span class="olstimept4">xxx</span></td>
-    <td><span class="confinterval olstimeub4">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb4">xxx</span></td>
-    <td><span class="olsr2pt4">xxx</span></td>
-    <td><span class="confinterval olsr2ub4">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">0.4247120479408722</span></td>
-    <td><span class="time">0.4280377244169049</span></td>
-    <td><span class="confinterval citime">0.43268214948319816</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">3.241674649906193e-3</span></td>
-    <td><span class="time">5.614496247321413e-3</span></td>
-    <td><span class="confinterval citime">8.471046026295666e-3</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have moderate
-     (<span class="percent">0.12244897959183673</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b5">bench/./gwvault-string.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde5" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time5" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle5" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb5">xxx</span></td>
-    <td><span class="olstimept5">xxx</span></td>
-    <td><span class="confinterval olstimeub5">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb5">xxx</span></td>
-    <td><span class="olsr2pt5">xxx</span></td>
-    <td><span class="confinterval olsr2ub5">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">5.923397785509686e-2</span></td>
-    <td><span class="time">5.948915604019876e-2</span></td>
-    <td><span class="confinterval citime">5.979412212483465e-2</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">4.0861921805163213e-4</span></td>
-    <td><span class="time">6.414232614480645e-4</span></td>
-    <td><span class="confinterval citime">1.0769747057569558e-3</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have slight
-     (<span class="percent">4.5351473922902494e-2</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b6">bench/./ansible-vault-file.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde6" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time6" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle6" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb6">xxx</span></td>
-    <td><span class="olstimept6">xxx</span></td>
-    <td><span class="confinterval olstimeub6">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb6">xxx</span></td>
-    <td><span class="olsr2pt6">xxx</span></td>
-    <td><span class="confinterval olsr2ub6">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">0.9063111016500747</span></td>
-    <td><span class="time">0.9137464516100469</span></td>
-    <td><span class="confinterval citime">0.9240750379100428</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">5.393903981006103e-3</span></td>
-    <td><span class="time">1.1205636220453591e-2</span></td>
-    <td><span class="confinterval citime">1.542422323897618e-2</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have moderate
-     (<span class="percent">0.16000000000000003</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b7">bench/./gwvault-file.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde7" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time7" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle7" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb7">xxx</span></td>
-    <td><span class="olstimept7">xxx</span></td>
-    <td><span class="confinterval olstimeub7">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb7">xxx</span></td>
-    <td><span class="olsr2pt7">xxx</span></td>
-    <td><span class="confinterval olsr2ub7">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">0.15430268911035178</span></td>
-    <td><span class="time">0.1556137329307144</span></td>
-    <td><span class="confinterval citime">0.15695866184990012</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">1.6966469325334782e-3</span></td>
-    <td><span class="time">2.5261353593158393e-3</span></td>
-    <td><span class="confinterval citime">3.811514561890576e-3</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have slight
-     (<span class="percent">7.100591715976311e-2</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b8">bench/./ansible-vault-rekey.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde8" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time8" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle8" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb8">xxx</span></td>
-    <td><span class="olstimept8">xxx</span></td>
-    <td><span class="confinterval olstimeub8">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb8">xxx</span></td>
-    <td><span class="olsr2pt8">xxx</span></td>
-    <td><span class="confinterval olsr2ub8">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">0.6628789246833398</span></td>
-    <td><span class="time">0.7216568147112058</span></td>
-    <td><span class="confinterval citime">0.8090466152946546</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">4.873506144831662e-2</span></td>
-    <td><span class="time">9.838676547648781e-2</span></td>
-    <td><span class="confinterval citime">0.1460107560009224</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have moderate
-     (<span class="percent">0.31776599608044276</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-<h2><a name="b9">bench/./gwvault-rekey.sh</a></h2>
- <table width="100%">
-  <tbody>
-   <tr>
-    <td><div id="kde9" class="kdechart"
-             style="width:450px;height:278px;"></div></td>
-    <td><div id="time9" class="timechart"
-             style="width:450px;height:278px;"></div></td>
-<!--
-    <td><div id="cycle9" class="cyclechart"
-             style="width:300px;height:278px;"></div></td>
--->
-   </tr>
-  </tbody>
- </table>
-
- <table>
-  <thead class="analysis">
-   <th></th>
-   <th class="cibound"
-       title="0.95 confidence level">lower bound</th>
-   <th>estimate</th>
-   <th class="cibound"
-       title="0.95 confidence level">upper bound</th>
-  </thead>
-  <tbody>
-   <tr>
-    <td>OLS regression</td>
-    <td><span class="confinterval olstimelb9">xxx</span></td>
-    <td><span class="olstimept9">xxx</span></td>
-    <td><span class="confinterval olstimeub9">xxx</span></td>
-   </tr>
-   <tr>
-    <td>R&#xb2; goodness-of-fit</td>
-    <td><span class="confinterval olsr2lb9">xxx</span></td>
-    <td><span class="olsr2pt9">xxx</span></td>
-    <td><span class="confinterval olsr2ub9">xxx</span></td>
-   </tr>
-   <tr>
-    <td>Mean execution time</td>
-    <td><span class="confinterval citime">0.1600520425469252</span></td>
-    <td><span class="time">0.162661628086318</span></td>
-    <td><span class="confinterval citime">0.16561311435429607</span></td>
-   </tr>
-   <tr>
-    <td>Standard deviation</td>
-    <td><span class="confinterval citime">3.8545745739126222e-3</span></td>
-    <td><span class="time">5.242671865187316e-3</span></td>
-    <td><span class="confinterval citime">7.0081533232793924e-3</span></td>
-   </tr>
-  </tbody>
- </table>
-
- <span class="outliers">
-   <p>Outlying measurements have slight
-     (<span class="percent">7.100591715976326e-2</span>%)
-     effect on estimated standard deviation.</p>
- </span>
-
- <h2><a name="grokularation">understanding this report</a></h2>
-
- <p>In this report, each function benchmarked by criterion is assigned
-   a section of its own.  The charts in each section are active; if
-   you hover your mouse over data points and annotations, you will see
-   more details.</p>
-
- <ul>
-   <li>The chart on the left is a
-     <a href="http://en.wikipedia.org/wiki/Kernel_density_estimation">kernel
-       density estimate</a> (also known as a KDE) of time
-     measurements.  This graphs the probability of any given time
-     measurement occurring.  A spike indicates that a measurement of a
-     particular time occurred; its height indicates how often that
-     measurement was repeated.</li>
-
-   <li>The chart on the right is the raw data from which the kernel
-     density estimate is built.  The <i>x</i> axis indicates the
-     number of loop iterations, while the <i>y</i> axis shows measured
-     execution time for the given number of loop iterations.  The
-     line behind the values is the linear regression prediction of
-     execution time for a given number of iterations. Ideally, all
-     measurements will be on (or very near) this line.</li>
- </ul>
-
- <p>Under the charts is a small table.
-   The first two rows are the results of a linear regression run
-     on the measurements displayed in the right-hand chart.</p>
-
- <ul>
-   <li><i>OLS regression</i> indicates the
-     time estimated for a single loop iteration using an ordinary
-     least-squares regression model.  This number is more accurate
-     than the <i>mean</i> estimate below it, as it more effectively
-     eliminates measurement overhead and other constant factors.</li>
-   <li><i>R&#xb2; goodness-of-fit</i> is a measure of how
-     accurately the linear regression model fits the observed
-     measurements.  If the measurements are not too noisy, R&#xb2;
-     should lie between 0.99 and 1, indicating an excellent fit. If
-     the number is below 0.99, something is confounding the accuracy
-     of the linear model.</li>
-   <li><i>Mean execution time</i> and <i>standard deviation</i> are
-     statistics calculated from execution time
-     divided by number of iterations.</li>
- </ul>
-
- <p>We use a statistical technique called
-   the <a href="http://en.wikipedia.org/wiki/Bootstrapping_(statistics)">bootstrap</a>
-   to provide confidence intervals on our estimates.  The
-   bootstrap-derived upper and lower bounds on estimates let you see
-   how accurate we believe those estimates to be.  (Hover the mouse
-   over the table headers to see the confidence levels.)</p>
-
- <p>A noisy benchmarking environment can cause some or many
-   measurements to fall far from the mean.  These outlying
-   measurements can have a significant inflationary effect on the
-   estimate of the standard deviation.  We calculate and display an
-   estimate of the extent to which the standard deviation has been
-   inflated by outliers.</p>
-
-<script type="text/javascript">
-$(function () {
-  function mangulate(rpt) {
-    var measured = function(key) {
-      var idx = rpt.reportKeys.indexOf(key);
-      return rpt.reportMeasured.map(function(r) { return r[idx]; });
-    };
-    var lowerBound = function(est) {
-      return est.estPoint - est.estError.confIntLDX;
-    };
-    var upperBound = function(est) {
-      return est.estPoint + est.estError.confIntUDX;
-    };
-    var number = rpt.reportNumber;
-    var name = rpt.reportName;
-    var mean = rpt.reportAnalysis.anMean.estPoint;
-    var iters = measured("iters");
-    var times = measured("time");
-    var kdetimes = rpt.reportKDEs[0].kdeValues;
-    var kdepdf = rpt.reportKDEs[0].kdePDF;
-
-    var meanSecs = mean;
-    var units = $.timeUnits(mean);
-    var rgrs = rpt.reportAnalysis.anRegress[0];
-    var scale = units[0];
-    var olsTime = rgrs.regCoeffs.iters;
-    $(".olstimept" + number).text(function() {
-        return $.renderTime(olsTime.estPoint);
-      });
-    $(".olstimelb" + number).text(function() {
-        return $.renderTime(lowerBound(olsTime));
-      });
-    $(".olstimeub" + number).text(function() {
-        return $.renderTime(upperBound(olsTime));
-      });
-    $(".olsr2pt" + number).text(function() {
-        return rgrs.regRSquare.estPoint.toFixed(3);
-      });
-    $(".olsr2lb" + number).text(function() {
-        return lowerBound(rgrs.regRSquare).toFixed(3);
-      });
-    $(".olsr2ub" + number).text(function() {
-        return upperBound(rgrs.regRSquare).toFixed(3);
-      });
-    mean *= scale;
-    kdetimes = $.scaleBy(scale, kdetimes);
-    var kq = $("#kde" + number);
-    var k = $.plot(kq,
-           [{ label: name + " time densities",
-              data: $.zip(kdetimes, kdepdf),
-              }],
-           { xaxis: { tickFormatter: $.unitFormatter(scale) },
-             yaxis: { ticks: false },
-             grid: { borderColor: "#777",
-                     hoverable: true, markings: [ { color: '#6fd3fb',
-                     lineWidth: 1.5, xaxis: { from: mean, to: mean } } ] },
-           });
-    var o = k.pointOffset({ x: mean, y: 0});
-    kq.append('<div class="meanlegend" title="' + $.renderTime(meanSecs) +
-              '" style="position:absolute;left:' + (o.left + 4) +
-              'px;bottom:139px;">mean</div>');
-    $.addTooltip("#kde" + number,
-                 function(secs) { return $.renderTime(secs / scale); });
-    var timepairs = new Array(times.length);
-    var lastiter = iters[iters.length-1];
-    var olspairs = [[0,0], [lastiter, lastiter * scale * olsTime.estPoint]];
-    for (var i = 0; i < times.length; i++)
-      timepairs[i] = [iters[i],times[i]*scale];
-    iterFormatter = function() {
-      var denom = 0;
-      return function(iters) {
-	if (iters == 0)
-          return '';
-	if (denom > 0)
-	  return (iters / denom).toFixed();
-        var power;
-	if (iters >= 1e9) {
-	    denom = '1e9'; power = '&#x2079;';
-        }
-	if (iters >= 1e6) {
-	    denom = '1e6'; power = '&#x2076;';
-        }
-        else if (iters >= 1e3) {
-            denom = '1e3'; power = '&#xb3;';
-        }
-        else denom = 1;
-        if (denom > 1) {
-          iters = (iters / denom).toFixed();
-	  iters += '&times;10' + power + ' iters';
-        } else {
-          iters += ' iters';
-        }
-        return iters;
-      };
-    };
-    $.plot($("#time" + number),
-           [{ label: "regression", data: olspairs,
-              lines: { show: true } },
-            { label: name + " times", data: timepairs,
-              points: { show: true } }],
-            { grid: { borderColor: "#777", hoverable: true },
-              xaxis: { tickFormatter: iterFormatter() },
-              yaxis: { tickFormatter: $.unitFormatter(scale) } });
-    $.addTooltip("#time" + number,
-		 function(iters,secs) {
-		   return ($.renderTime(secs / scale) + ' / ' +
-			   iters.toLocaleString() + ' iters');
-		 });
-    if (0) {
-      var cyclepairs = new Array(cycles.length);
-      for (var i = 0; i < cycles.length; i++)
-	cyclepairs[i] = [cycles[i],i];
-      $.plot($("#cycle" + number),
-	     [{ label: name + " cycles",
-		data: cyclepairs }],
-	     { points: { show: true },
-	       grid: { borderColor: "#777", hoverable: true },
-	       xaxis: { tickFormatter:
-			function(cycles,axis) { return cycles + ' cycles'; }},
-	       yaxis: { ticks: false },
-	     });
-      $.addTooltip("#cycles" + number, function(x,y) { return x + ' cycles'; });
-    }
-  };
-  var reports = 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-  reports.map(mangulate);
-
-  var benches = ["bench/./ansible-vault-encrypt.sh","bench/./gwvault-encrypt.sh","bench/./ansible-vault-decrypt.sh","bench/./gwvault-decrypt.sh","bench/./ansible-vault-string.sh","bench/./gwvault-string.sh","bench/./ansible-vault-file.sh","bench/./gwvault-file.sh","bench/./ansible-vault-rekey.sh","bench/./gwvault-rekey.sh",];
-  var ylabels = [[-0,"<a href=\"#b0\">bench/./ansible-vault-encrypt.sh</a>"],[-1,"<a href=\"#b1\">bench/./gwvault-encrypt.sh</a>"],[-2,"<a href=\"#b2\">bench/./ansible-vault-decrypt.sh</a>"],[-3,"<a href=\"#b3\">bench/./gwvault-decrypt.sh</a>"],[-4,"<a href=\"#b4\">bench/./ansible-vault-string.sh</a>"],[-5,"<a href=\"#b5\">bench/./gwvault-string.sh</a>"],[-6,"<a href=\"#b6\">bench/./ansible-vault-file.sh</a>"],[-7,"<a href=\"#b7\">bench/./gwvault-file.sh</a>"],[-8,"<a href=\"#b8\">bench/./ansible-vault-rekey.sh</a>"],[-9,"<a href=\"#b9\">bench/./gwvault-rekey.sh</a>"],];
-  var means = $.scaleTimes([0.4908948086617951,8.8629525776332e-2,0.506753976247042,9.602444657299489e-2,0.4280377244169049,5.948915604019876e-2,0.9137464516100469,0.1556137329307144,0.7216568147112058,0.162661628086318,]);
-  var stddevs = [1.3673801077204834e-2,9.972547118101713e-4,5.0367134962777595e-2,2.114096929002399e-3,5.614496247321413e-3,6.414232614480645e-4,1.1205636220453591e-2,2.5261353593158393e-3,9.838676547648781e-2,5.242671865187316e-3,];
-  stddevs = $.scaleBy(means[1], stddevs);
-  var xs = [];
-  var prev = null;
-  var colors = ["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"];
-  var max_time = 0;
-  for (var i = 0; i < means[0].length; i++) {
-    var name = benches[i].split(/\//);
-    name.pop();
-    name = name.join('/');
-    if (name != prev) {
-      var color = colors[xs.length / 2 % colors.length];
-      xs.push({ data: [[means[0][i], -i]], label: name, color: color,
-                bars: { show: true, horizontal: true, barWidth: 0.75,
-                        align: "center" } });
-      xs.push({ data: [[means[0][i], -i, stddevs[i]]], color: color,
-                lines: { show: false },
-                points: { radius: 0, errorbars: "x",
-                          xerr: { show: true, radius: 5,
-                                  lowerCap: "-", upperCap: "-" } } });
-      prev = name;
-    } else {
-      xs[xs.length - 2].data.push([means[0][i], -i]);
-      xs[xs.length - 1].data.push([means[0][i], -i, stddevs[i]]);
-    }
-    max_time = Math.max(max_time, means[0][i] + stddevs[i]);
-  }
-  var oq = $("#overview");
-  var max_x = max_time * 1.02;
-  o = $.plot(oq, xs, { grid: { borderColor: "#777", hoverable: true },
-                       legend: { show: xs.length > 1 },
-                       xaxis: { max: max_x, zoomRange: [false, max_x], panRange: [0, max_x] },
-                       yaxis: { ticks: ylabels, tickColor: '#ffffff', zoomRange: false, panRange: false },
-                       zoom: { interactive: true, amount: 1.05 },
-                       pan: { interactive: true } });
-  if (benches.length > 3)
-    o.getPlaceholder().height(28*benches.length);
-  o.resize();
-  o.setupGrid();
-  o.draw();
-  $.addTooltip("#overview", function(x,y) { return $.renderTime(x / means[1]); });
-});
-$(document).ready(function () {
-    $(".time").text(function(_, text) {
-        return $.renderTime(text);
-      });
-    $(".citime").text(function(_, text) {
-        return $.renderTime(text);
-      });
-    $(".percent").text(function(_, text) {
-        return (text*100).toFixed(1);
-      });
-  });
-</script>
-
-   </div>
-  </div>
-  <div id="footer">
-    <div class="body">
-     <div class="footfirst">
-      <h2>colophon</h2>
-      <p>This report was created using the
-	<a href="http://hackage.haskell.org/package/criterion">criterion</a>
-	benchmark execution and performance analysis tool.</p>
-      <p>Criterion is developed and maintained
-      by <a href="http://www.serpentine.com/blog/">Bryan O'Sullivan</a>.</p>
-     </div>
-    </div>
-  </div>
- </body>
-</html>

	lower bound	estimate	upper bound
OLS regression	xxx	xxx	xxx
R² goodness-of-fit	xxx	xxx	xxx
Mean execution time	0.4847128241570614	0.4908948086617951	0.5109107636187348
Standard deviation	4.176360362081773e-3	1.3673801077204834e-2	2.0399166016711115e-2