hen/bench.html at master · nazrhom/hen · GitHub

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<html>
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    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
    <title>criterion report</title>
    <script language="javascript" type="text/javascript">
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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 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    <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">initial position/depth: 2 White</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">2.3572417059665994e-4</span></td>
    <td><span class="time">2.4242454549205355e-4</span></td>
    <td><span class="confinterval citime">2.507577890430476e-4</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">2.2818395473233846e-5</span></td>
    <td><span class="time">2.641155123751754e-5</span></td>
    <td><span class="confinterval citime">2.8657170365375155e-5</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have severe
     (<span class="percent">0.8191604619588299</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b1">initial position/depth: 5 White</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">0.12298010281540753</span></td>
    <td><span class="time">0.1339881416448914</span></td>
    <td><span class="confinterval citime">0.1489585926817638</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.4687096215843411e-2</span></td>
    <td><span class="time">1.846448527616097e-2</span></td>
    <td><span class="confinterval citime">2.3112025197591604e-2</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.4069827166264064</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b2">initial position/depth: 6 White</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">1.293439303478408</span></td>
    <td><span class="time">1.3358972489795633</span></td>
    <td><span class="confinterval citime">1.3585872546682367</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">7.327212166273966e-3</span></td>
    <td><span class="time">4.0501669449067454e-2</span></td>
    <td><span class="confinterval citime">5.240032356907893e-2</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.18749999999999997</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b3">initial position/depth: 7 White</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">15.800547604249612</span></td>
    <td><span class="time">16.29457240493836</span></td>
    <td><span class="confinterval citime">16.592218061625925</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.22396987007101027</span></td>
    <td><span class="time">0.49445066412378685</span></td>
    <td><span class="confinterval citime">0.6873832000467521</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.1875</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>

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   </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>
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