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i=0,position;position=positions[i];i++){if(position.x==x&&position.y==y){position.active=true;return}}position={active:true,rendered:false,element:positions.length?info.element.clone():info.element,x:x,y:y};positions.push(position);position.element.css({top:Math.round(y),left:Math.round(x),"text-align":halign})};Canvas.prototype.removeText=function(layer,x,y,text,font,angle){if(text==null){var layerCache=this._textCache[layer];if(layerCache!=null){for(var styleKey in layerCache){if(hasOwnProperty.call(layerCache,styleKey)){var styleCache=layerCache[styleKey];for(var key in styleCache){if(hasOwnProperty.call(styleCache,key)){var positions=styleCache[key].positions;for(var i=0,position;position=positions[i];i++){position.active=false}}}}}}}else{var positions=this.getTextInfo(layer,text,font,angle).positions;for(var i=0,position;position=positions[i];i++){if(position.x==x&&position.y==y){position.active=false}}}};function Plot(placeholder,data_,options_,plugins){var series=[],options={colors:["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"],legend:{show:true,noColumns:1,labelFormatter:null,labelBoxBorderColor:"#ccc",container:null,position:"ne",margin:5,backgroundColor:null,backgroundOpacity:.85,sorted:null},xaxis:{show:null,position:"bottom",mode:null,font:null,color:null,tickColor:null,transform:null,inverseTransform:null,min:null,max:null,autoscaleMargin:null,ticks:null,tickFormatter:null,labelWidth:null,labelHeight:null,reserveSpace:null,tickLength:null,alignTicksWithAxis:null,tickDecimals:null,tickSize:null,minTickSize:null},yaxis:{autoscaleMargin:.02,position:"left"},xaxes:[],yaxes:[],series:{points:{show:false,radius:3,lineWidth:2,fill:true,fillColor:"#ffffff",symbol:"circle"},lines:{lineWidth:2,fill:false,fillColor:null,steps:false},bars:{show:false,lineWidth:2,barWidth:1,fill:true,fillColor:null,align:"left",horizontal:false,zero:true},shadowSize:3,highlightColor:null},grid:{show:true,aboveData:false,color:"#545454",backgroundColor:null,borderColor:null,tickColor:null,margin:0,labelMargin:5,axisMargin:8,borderWidth:2,minBorderMargin:null,markings:null,markingsColor:"#f4f4f4",markingsLineWidth:2,clickable:false,hoverable:false,autoHighlight:true,mouseActiveRadius:10},interaction:{redrawOverlayInterval:1e3/60},hooks:{}},surface=null,overlay=null,eventHolder=null,ctx=null,octx=null,xaxes=[],yaxes=[],plotOffset={left:0,right:0,top:0,bottom:0},plotWidth=0,plotHeight=0,hooks={processOptions:[],processRawData:[],processDatapoints:[],processOffset:[],drawBackground:[],drawSeries:[],draw:[],bindEvents:[],drawOverlay:[],shutdown:[]},plot=this;plot.setData=setData;plot.setupGrid=setupGrid;plot.draw=draw;plot.getPlaceholder=function(){return 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=$.color.parse(colorPool[i%colorPoolSize]||"#666");if(i%colorPoolSize==0&&i){if(variation>=0){if(variation<.5){variation=-variation-.2}else variation=0}else variation=-variation}colors[i]=c.scale("rgb",1+variation)}var colori=0,s;for(i=0;i<series.length;++i){s=series[i];if(s.color==null){s.color=colors[colori].toString();++colori}else if(typeof s.color=="number")s.color=colors[s.color].toString();if(s.lines.show==null){var v,show=true;for(v in s)if(s[v]&&s[v].show){show=false;break}if(show)s.lines.show=true}if(s.lines.zero==null){s.lines.zero=!!s.lines.fill}s.xaxis=getOrCreateAxis(xaxes,axisNumber(s,"x"));s.yaxis=getOrCreateAxis(yaxes,axisNumber(s,"y"))}}function processData(){var topSentry=Number.POSITIVE_INFINITY,bottomSentry=Number.NEGATIVE_INFINITY,fakeInfinity=Number.MAX_VALUE,i,j,k,m,length,s,points,ps,x,y,axis,val,f,p,data,format;function updateAxis(axis,min,max){if(min<axis.datamin&&min!=-fakeInfinity)axis.datamin=min;if(max>axis.datamax&&max!=fakeInfinity)axis.datamax=max}$.each(allAxes(),function(_,axis){axis.datamin=topSentry;axis.datamax=bottomSentry;axis.used=false});for(i=0;i<series.length;++i){s=series[i];s.datapoints={points:[]};executeHooks(hooks.processRawData,[s,s.data,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];data=s.data;format=s.datapoints.format;if(!format){format=[];format.push({x:true,number:true,required:true});format.push({y:true,number:true,required:true});if(s.bars.show||s.lines.show&&s.lines.fill){var autoscale=!!(s.bars.show&&s.bars.zero||s.lines.show&&s.lines.zero);format.push({y:true,number:true,required:false,defaultValue:0,autoscale:autoscale});if(s.bars.horizontal){delete 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)}$.each(allAxes(),function(_,axis){if(axis.datamin==topSentry)axis.datamin=null;if(axis.datamax==bottomSentry)axis.datamax=null})}function setupCanvases(){placeholder.css("padding",0).children().filter(function(){return!$(this).hasClass("flot-overlay")&&!$(this).hasClass("flot-base")}).remove();if(placeholder.css("position")=="static")placeholder.css("position","relative");surface=new Canvas("flot-base",placeholder);overlay=new Canvas("flot-overlay",placeholder);ctx=surface.context;octx=overlay.context;eventHolder=$(overlay.element).unbind();var existing=placeholder.data("plot");if(existing){existing.shutdown();overlay.clear()}placeholder.data("plot",plot)}function bindEvents(){if(options.grid.hoverable){eventHolder.mousemove(onMouseMove);eventHolder.bind("mouseleave",onMouseLeave)}if(options.grid.clickable)eventHolder.click(onClick);executeHooks(hooks.bindEvents,[eventHolder])}function shutdown(){if(redrawTimeout)clearTimeout(redrawTimeout);eventHolder.unbind("mousemove",onMouseMove);eventHolder.unbind("mouseleave",onMouseLeave);eventHolder.unbind("click",onClick);executeHooks(hooks.shutdown,[eventHolder])}function 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;
21if(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 getFillStyle(series.bars,series.color,bottom,top)}:null;plotBars(series.datapoints,barLeft,barLeft+series.bars.barWidth,fillStyleCallback,series.xaxis,series.yaxis);ctx.restore()}function getFillStyle(filloptions,seriesColor,bottom,top){var fill=filloptions.fill;if(!fill)return null;if(filloptions.fillColor)return getColorOrGradient(filloptions.fillColor,bottom,top,seriesColor);var c=$.color.parse(seriesColor);c.a=typeof fill=="number"?fill:.4;c.normalize();return c.toString()}function insertLegend(){if(options.legend.container!=null){$(options.legend.container).html("")}else{placeholder.find(".legend").remove()}if(!options.legend.show){return}var fragments=[],entries=[],rowStarted=false,lf=options.legend.labelFormatter,s,label;for(var i=0;i<series.length;++i){s=series[i];if(s.label){label=lf?lf(s.label,s):s.label;if(label){entries.push({label:label,color:s.color})}}}if(options.legend.sorted){if($.isFunction(options.legend.sorted)){entries.sort(options.legend.sorted)}else 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264 <h1>criterion performance measurements</h1>
268<p><a href="#grokularation">want to understand this report?</a></p>
270<p>Environment: AMD Opteron 6168 on Ubuntu 12.04 with GHC 8.0.2</p>
272<div id="overview" class="ovchart" style="width:900px;height:100px;"></div>
274<h2><a name="b0">create/relation 10x100</a></h2>
278 <td><div id="kde0" class="kdechart"
279 style="width:450px;height:278px;"></div></td>
280 <td><div id="time0" class="timechart"
281 style="width:450px;height:278px;"></div></td>
283 <td><div id="cycle0" class="cyclechart"
284 style="width:300px;height:278px;"></div></td>
291 <thead class="analysis">
294 title="0.95 confidence level">lower bound</th>
297 title="0.95 confidence level">upper bound</th>
301 <td>OLS regression</td>
302 <td><span class="confinterval olstimelb0">xxx</span></td>
303 <td><span class="olstimept0">xxx</span></td>
304 <td><span class="confinterval olstimeub0">xxx</span></td>
307 <td>R² goodness-of-fit</td>
308 <td><span class="confinterval olsr2lb0">xxx</span></td>
309 <td><span class="olsr2pt0">xxx</span></td>
310 <td><span class="confinterval olsr2ub0">xxx</span></td>
313 <td>Mean execution time</td>
314 <td><span class="confinterval citime">5.891481267739451e-4</span></td>
315 <td><span class="time">5.906283388143756e-4</span></td>
316 <td><span class="confinterval citime">5.919056866982677e-4</span></td>
319 <td>Standard deviation</td>
320 <td><span class="confinterval citime">3.7867077713250133e-6</span></td>
321 <td><span class="time">4.502571301832575e-6</span></td>
322 <td><span class="confinterval citime">5.8062391798041006e-6</span></td>
327 <span class="outliers">
328 <p>Outlying measurements have slight
329 (<span class="percent">1.2343749999999981e-2</span>%)
330 effect on estimated standard deviation.</p>
332<h2><a name="b1">create/relation 10x1000</a></h2>
336 <td><div id="kde1" class="kdechart"
337 style="width:450px;height:278px;"></div></td>
338 <td><div id="time1" class="timechart"
339 style="width:450px;height:278px;"></div></td>
341 <td><div id="cycle1" class="cyclechart"
342 style="width:300px;height:278px;"></div></td>
349 <thead class="analysis">
352 title="0.95 confidence level">lower bound</th>
355 title="0.95 confidence level">upper bound</th>
359 <td>OLS regression</td>
360 <td><span class="confinterval olstimelb1">xxx</span></td>
361 <td><span class="olstimept1">xxx</span></td>
362 <td><span class="confinterval olstimeub1">xxx</span></td>
365 <td>R² goodness-of-fit</td>
366 <td><span class="confinterval olsr2lb1">xxx</span></td>
367 <td><span class="olsr2pt1">xxx</span></td>
368 <td><span class="confinterval olsr2ub1">xxx</span></td>
371 <td>Mean execution time</td>
372 <td><span class="confinterval citime">5.760191573102873e-3</span></td>
373 <td><span class="time">5.770838309782314e-3</span></td>
374 <td><span class="confinterval citime">5.779948952798578e-3</span></td>
377 <td>Standard deviation</td>
378 <td><span class="confinterval citime">2.2935863601065537e-5</span></td>
379 <td><span class="time">2.8815215559732134e-5</span></td>
380 <td><span class="confinterval citime">3.914144050420156e-5</span></td>
385 <span class="outliers">
386 <p>Outlying measurements have slight
387 (<span class="percent">2.5623268698060614e-2</span>%)
388 effect on estimated standard deviation.</p>
390<h2><a name="b2">create/relation 10x10000</a></h2>
394 <td><div id="kde2" class="kdechart"
395 style="width:450px;height:278px;"></div></td>
396 <td><div id="time2" class="timechart"
397 style="width:450px;height:278px;"></div></td>
399 <td><div id="cycle2" class="cyclechart"
400 style="width:300px;height:278px;"></div></td>
407 <thead class="analysis">
410 title="0.95 confidence level">lower bound</th>
413 title="0.95 confidence level">upper bound</th>
417 <td>OLS regression</td>
418 <td><span class="confinterval olstimelb2">xxx</span></td>
419 <td><span class="olstimept2">xxx</span></td>
420 <td><span class="confinterval olstimeub2">xxx</span></td>
423 <td>R² goodness-of-fit</td>
424 <td><span class="confinterval olsr2lb2">xxx</span></td>
425 <td><span class="olsr2pt2">xxx</span></td>
426 <td><span class="confinterval olsr2ub2">xxx</span></td>
429 <td>Mean execution time</td>
430 <td><span class="confinterval citime">5.772489494946795e-2</span></td>
431 <td><span class="time">5.784195729887314e-2</span></td>
432 <td><span class="confinterval citime">5.792298187695035e-2</span></td>
435 <td>Standard deviation</td>
436 <td><span class="confinterval citime">1.0422507194091924e-4</span></td>
437 <td><span class="time">1.7121491767487851e-4</span></td>
438 <td><span class="confinterval citime">2.73724491538287e-4</span></td>
443 <span class="outliers">
444 <p>Outlying measurements have slight
445 (<span class="percent">7.638888888888888e-2</span>%)
446 effect on estimated standard deviation.</p>
448<h2><a name="b3">restrict/restrict relation 10x10000 to 10x1</a></h2>
452 <td><div id="kde3" class="kdechart"
453 style="width:450px;height:278px;"></div></td>
454 <td><div id="time3" class="timechart"
455 style="width:450px;height:278px;"></div></td>
457 <td><div id="cycle3" class="cyclechart"
458 style="width:300px;height:278px;"></div></td>
465 <thead class="analysis">
468 title="0.95 confidence level">lower bound</th>
471 title="0.95 confidence level">upper bound</th>
475 <td>OLS regression</td>
476 <td><span class="confinterval olstimelb3">xxx</span></td>
477 <td><span class="olstimept3">xxx</span></td>
478 <td><span class="confinterval olstimeub3">xxx</span></td>
481 <td>R² goodness-of-fit</td>
482 <td><span class="confinterval olsr2lb3">xxx</span></td>
483 <td><span class="olsr2pt3">xxx</span></td>
484 <td><span class="confinterval olsr2ub3">xxx</span></td>
487 <td>Mean execution time</td>
488 <td><span class="confinterval citime">1.4337830338577313e-2</span></td>
489 <td><span class="time">1.448797309139284e-2</span></td>
490 <td><span class="confinterval citime">1.4763601823824296e-2</span></td>
493 <td>Standard deviation</td>
494 <td><span class="confinterval citime">2.772536754203617e-4</span></td>
495 <td><span class="time">4.774249175945004e-4</span></td>
496 <td><span class="confinterval citime">8.291091461013828e-4</span></td>
501 <span class="outliers">
502 <p>Outlying measurements have moderate
503 (<span class="percent">0.11563227985262721</span>%)
504 effect on estimated standard deviation.</p>
506<h2><a name="b4">restrict/restrict relation 10x10000 to 10x5000</a></h2>
510 <td><div id="kde4" class="kdechart"
511 style="width:450px;height:278px;"></div></td>
512 <td><div id="time4" class="timechart"
513 style="width:450px;height:278px;"></div></td>
515 <td><div id="cycle4" class="cyclechart"
516 style="width:300px;height:278px;"></div></td>
523 <thead class="analysis">
526 title="0.95 confidence level">lower bound</th>
529 title="0.95 confidence level">upper bound</th>
533 <td>OLS regression</td>
534 <td><span class="confinterval olstimelb4">xxx</span></td>
535 <td><span class="olstimept4">xxx</span></td>
536 <td><span class="confinterval olstimeub4">xxx</span></td>
539 <td>R² goodness-of-fit</td>
540 <td><span class="confinterval olsr2lb4">xxx</span></td>
541 <td><span class="olsr2pt4">xxx</span></td>
542 <td><span class="confinterval olsr2ub4">xxx</span></td>
545 <td>Mean execution time</td>
546 <td><span class="confinterval citime">8.322941573263729e-2</span></td>
547 <td><span class="time">8.447258684075218e-2</span></td>
548 <td><span class="confinterval citime">8.511386931168204e-2</span></td>
551 <td>Standard deviation</td>
552 <td><span class="confinterval citime">6.118316959029327e-4</span></td>
553 <td><span class="time">1.4013810341920257e-3</span></td>
554 <td><span class="confinterval citime">2.3016514403696887e-3</span></td>
559 <span class="outliers">
560 <p>Outlying measurements have slight
561 (<span class="percent">9.0e-2</span>%)
562 effect on estimated standard deviation.</p>
564<h2><a name="b5">project/project 10x1000 to 1x1000</a></h2>
568 <td><div id="kde5" class="kdechart"
569 style="width:450px;height:278px;"></div></td>
570 <td><div id="time5" class="timechart"
571 style="width:450px;height:278px;"></div></td>
573 <td><div id="cycle5" class="cyclechart"
574 style="width:300px;height:278px;"></div></td>
581 <thead class="analysis">
584 title="0.95 confidence level">lower bound</th>
587 title="0.95 confidence level">upper bound</th>
591 <td>OLS regression</td>
592 <td><span class="confinterval olstimelb5">xxx</span></td>
593 <td><span class="olstimept5">xxx</span></td>
594 <td><span class="confinterval olstimeub5">xxx</span></td>
597 <td>R² goodness-of-fit</td>
598 <td><span class="confinterval olsr2lb5">xxx</span></td>
599 <td><span class="olsr2pt5">xxx</span></td>
600 <td><span class="confinterval olsr2ub5">xxx</span></td>
603 <td>Mean execution time</td>
604 <td><span class="confinterval citime">1.2211661066456561e-2</span></td>
605 <td><span class="time">1.243577405705988e-2</span></td>
606 <td><span class="confinterval citime">1.2583044006972743e-2</span></td>
609 <td>Standard deviation</td>
610 <td><span class="confinterval citime">3.0211460384957275e-4</span></td>
611 <td><span class="time">4.600204612133745e-4</span></td>
612 <td><span class="confinterval citime">5.956804020691042e-4</span></td>
617 <span class="outliers">
618 <p>Outlying measurements have moderate
619 (<span class="percent">0.14345146934193906</span>%)
620 effect on estimated standard deviation.</p>
622<h2><a name="b6">project/project 10x1000 to 5x1000</a></h2>
626 <td><div id="kde6" class="kdechart"
627 style="width:450px;height:278px;"></div></td>
628 <td><div id="time6" class="timechart"
629 style="width:450px;height:278px;"></div></td>
631 <td><div id="cycle6" class="cyclechart"
632 style="width:300px;height:278px;"></div></td>
639 <thead class="analysis">
642 title="0.95 confidence level">lower bound</th>
645 title="0.95 confidence level">upper bound</th>
649 <td>OLS regression</td>
650 <td><span class="confinterval olstimelb6">xxx</span></td>
651 <td><span class="olstimept6">xxx</span></td>
652 <td><span class="confinterval olstimeub6">xxx</span></td>
655 <td>R² goodness-of-fit</td>
656 <td><span class="confinterval olsr2lb6">xxx</span></td>
657 <td><span class="olsr2pt6">xxx</span></td>
658 <td><span class="confinterval olsr2ub6">xxx</span></td>
661 <td>Mean execution time</td>
662 <td><span class="confinterval citime">1.5271674270594341e-2</span></td>
663 <td><span class="time">1.547382196820991e-2</span></td>
664 <td><span class="confinterval citime">1.5622307025724294e-2</span></td>
667 <td>Standard deviation</td>
668 <td><span class="confinterval citime">2.873217405621163e-4</span></td>
669 <td><span class="time">3.978143402171812e-4</span></td>
670 <td><span class="confinterval citime">5.247713511789067e-4</span></td>
675 <span class="outliers">
676 <p>Outlying measurements have slight
677 (<span class="percent">4.1587901701323225e-2</span>%)
678 effect on estimated standard deviation.</p>
680<h2><a name="b7">union/union identical 10x1000</a></h2>
684 <td><div id="kde7" class="kdechart"
685 style="width:450px;height:278px;"></div></td>
686 <td><div id="time7" class="timechart"
687 style="width:450px;height:278px;"></div></td>
689 <td><div id="cycle7" class="cyclechart"
690 style="width:300px;height:278px;"></div></td>
697 <thead class="analysis">
700 title="0.95 confidence level">lower bound</th>
703 title="0.95 confidence level">upper bound</th>
707 <td>OLS regression</td>
708 <td><span class="confinterval olstimelb7">xxx</span></td>
709 <td><span class="olstimept7">xxx</span></td>
710 <td><span class="confinterval olstimeub7">xxx</span></td>
713 <td>R² goodness-of-fit</td>
714 <td><span class="confinterval olsr2lb7">xxx</span></td>
715 <td><span class="olsr2pt7">xxx</span></td>
716 <td><span class="confinterval olsr2ub7">xxx</span></td>
719 <td>Mean execution time</td>
720 <td><span class="confinterval citime">4.908323998442862e-2</span></td>
721 <td><span class="time">4.923173514046889e-2</span></td>
722 <td><span class="confinterval citime">4.94547578201357e-2</span></td>
725 <td>Standard deviation</td>
726 <td><span class="confinterval citime">2.09615306506087e-4</span></td>
727 <td><span class="time">3.275115670368287e-4</span></td>
728 <td><span class="confinterval citime">4.977889379852797e-4</span></td>
733 <span class="outliers">
734 <p>Outlying measurements have slight
735 (<span class="percent">7.100591715976319e-2</span>%)
736 effect on estimated standard deviation.</p>
738<h2><a name="b8">union/union identical 10x10000</a></h2>
742 <td><div id="kde8" class="kdechart"
743 style="width:450px;height:278px;"></div></td>
744 <td><div id="time8" class="timechart"
745 style="width:450px;height:278px;"></div></td>
747 <td><div id="cycle8" class="cyclechart"
748 style="width:300px;height:278px;"></div></td>
755 <thead class="analysis">
758 title="0.95 confidence level">lower bound</th>
761 title="0.95 confidence level">upper bound</th>
765 <td>OLS regression</td>
766 <td><span class="confinterval olstimelb8">xxx</span></td>
767 <td><span class="olstimept8">xxx</span></td>
768 <td><span class="confinterval olstimeub8">xxx</span></td>
771 <td>R² goodness-of-fit</td>
772 <td><span class="confinterval olsr2lb8">xxx</span></td>
773 <td><span class="olsr2pt8">xxx</span></td>
774 <td><span class="confinterval olsr2ub8">xxx</span></td>
777 <td>Mean execution time</td>
778 <td><span class="confinterval citime">0.5144192261126985</span></td>
779 <td><span class="time">0.5157808375258943</span></td>
780 <td><span class="confinterval citime">0.5164000385072457</span></td>
783 <td>Standard deviation</td>
784 <td><span class="confinterval citime">0</span></td>
785 <td><span class="time">1.1935851498586624e-3</span></td>
786 <td><span class="confinterval citime">1.285892588023751e-3</span></td>
791 <span class="outliers">
792 <p>Outlying measurements have moderate
793 (<span class="percent">0.18749999999999997</span>%)
794 effect on estimated standard deviation.</p>
796<h2><a name="b9">join/join identical 10x100</a></h2>
800 <td><div id="kde9" class="kdechart"
801 style="width:450px;height:278px;"></div></td>
802 <td><div id="time9" class="timechart"
803 style="width:450px;height:278px;"></div></td>
805 <td><div id="cycle9" class="cyclechart"
806 style="width:300px;height:278px;"></div></td>
813 <thead class="analysis">
816 title="0.95 confidence level">lower bound</th>
819 title="0.95 confidence level">upper bound</th>
823 <td>OLS regression</td>
824 <td><span class="confinterval olstimelb9">xxx</span></td>
825 <td><span class="olstimept9">xxx</span></td>
826 <td><span class="confinterval olstimeub9">xxx</span></td>
829 <td>R² goodness-of-fit</td>
830 <td><span class="confinterval olsr2lb9">xxx</span></td>
831 <td><span class="olsr2pt9">xxx</span></td>
832 <td><span class="confinterval olsr2ub9">xxx</span></td>
835 <td>Mean execution time</td>
836 <td><span class="confinterval citime">0.3355841931137941</span></td>
837 <td><span class="time">0.3374978199984254</span></td>
838 <td><span class="confinterval citime">0.3385756312293291</span></td>
841 <td>Standard deviation</td>
842 <td><span class="confinterval citime">0</span></td>
843 <td><span class="time">1.6965376723717264e-3</span></td>
844 <td><span class="confinterval citime">1.8668238128934738e-3</span></td>
849 <span class="outliers">
850 <p>Outlying measurements have moderate
851 (<span class="percent">0.1875</span>%)
852 effect on estimated standard deviation.</p>
854<h2><a name="b10">group/group 10x100</a></h2>
858 <td><div id="kde10" class="kdechart"
859 style="width:450px;height:278px;"></div></td>
860 <td><div id="time10" class="timechart"
861 style="width:450px;height:278px;"></div></td>
863 <td><div id="cycle10" class="cyclechart"
864 style="width:300px;height:278px;"></div></td>
871 <thead class="analysis">
874 title="0.95 confidence level">lower bound</th>
877 title="0.95 confidence level">upper bound</th>
881 <td>OLS regression</td>
882 <td><span class="confinterval olstimelb10">xxx</span></td>
883 <td><span class="olstimept10">xxx</span></td>
884 <td><span class="confinterval olstimeub10">xxx</span></td>
887 <td>R² goodness-of-fit</td>
888 <td><span class="confinterval olsr2lb10">xxx</span></td>
889 <td><span class="olsr2pt10">xxx</span></td>
890 <td><span class="confinterval olsr2ub10">xxx</span></td>
893 <td>Mean execution time</td>
894 <td><span class="confinterval citime">4.666001885635249e-2</span></td>
895 <td><span class="time">4.70336206675725e-2</span></td>
896 <td><span class="confinterval citime">4.7476184359486216e-2</span></td>
899 <td>Standard deviation</td>
900 <td><span class="confinterval citime">5.178696454087703e-4</span></td>
901 <td><span class="time">7.389605929837668e-4</span></td>
902 <td><span class="confinterval citime">1.1404867684675546e-3</span></td>
907 <span class="outliers">
908 <p>Outlying measurements have slight
909 (<span class="percent">7.100591715976322e-2</span>%)
910 effect on estimated standard deviation.</p>
912<h2><a name="b11">write/write 10x1000</a></h2>
916 <td><div id="kde11" class="kdechart"
917 style="width:450px;height:278px;"></div></td>
918 <td><div id="time11" class="timechart"
919 style="width:450px;height:278px;"></div></td>
921 <td><div id="cycle11" class="cyclechart"
922 style="width:300px;height:278px;"></div></td>
929 <thead class="analysis">
932 title="0.95 confidence level">lower bound</th>
935 title="0.95 confidence level">upper bound</th>
939 <td>OLS regression</td>
940 <td><span class="confinterval olstimelb11">xxx</span></td>
941 <td><span class="olstimept11">xxx</span></td>
942 <td><span class="confinterval olstimeub11">xxx</span></td>
945 <td>R² goodness-of-fit</td>
946 <td><span class="confinterval olsr2lb11">xxx</span></td>
947 <td><span class="olsr2pt11">xxx</span></td>
948 <td><span class="confinterval olsr2ub11">xxx</span></td>
951 <td>Mean execution time</td>
952 <td><span class="confinterval citime">1.5472953190097016e-3</span></td>
953 <td><span class="time">1.5673276303702817e-3</span></td>
954 <td><span class="confinterval citime">1.5890440248897237e-3</span></td>
957 <td>Standard deviation</td>
958 <td><span class="confinterval citime">6.110376965683773e-5</span></td>
959 <td><span class="time">6.970009193368111e-5</span></td>
960 <td><span class="confinterval citime">7.954403273020534e-5</span></td>
965 <span class="outliers">
966 <p>Outlying measurements have moderate
967 (<span class="percent">0.30346203307926556</span>%)
968 effect on estimated standard deviation.</p>
971 <h2><a name="grokularation">understanding this report</a></h2>
973 <p>In this report, each function benchmarked by criterion is assigned
974 a section of its own. The charts in each section are active; if
975 you hover your mouse over data points and annotations, you will see
979 <li>The chart on the left is a
980 <a href="http://en.wikipedia.org/wiki/Kernel_density_estimation">kernel
981 density estimate</a> (also known as a KDE) of time
982 measurements. This graphs the probability of any given time
983 measurement occurring. A spike indicates that a measurement of a
984 particular time occurred; its height indicates how often that
985 measurement was repeated.</li>
987 <li>The chart on the right is the raw data from which the kernel
988 density estimate is built. The <i>x</i> axis indicates the
989 number of loop iterations, while the <i>y</i> axis shows measured
990 execution time for the given number of loop iterations. The
991 line behind the values is the linear regression prediction of
992 execution time for a given number of iterations. Ideally, all
993 measurements will be on (or very near) this line.</li>
996 <p>Under the charts is a small table.
997 The first two rows are the results of a linear regression run
998 on the measurements displayed in the right-hand chart.</p>
1001 <li><i>OLS regression</i> indicates the
1002 time estimated for a single loop iteration using an ordinary
1003 least-squares regression model. This number is more accurate
1004 than the <i>mean</i> estimate below it, as it more effectively
1005 eliminates measurement overhead and other constant factors.</li>
1006 <li><i>R² goodness-of-fit</i> is a measure of how
1007 accurately the linear regression model fits the observed
1008 measurements. If the measurements are not too noisy, R²
1009 should lie between 0.99 and 1, indicating an excellent fit. If
1010 the number is below 0.99, something is confounding the accuracy
1011 of the linear model.</li>
1012 <li><i>Mean execution time</i> and <i>standard deviation</i> are
1013 statistics calculated from execution time
1014 divided by number of iterations.</li>
1017 <p>We use a statistical technique called
1018 the <a href="http://en.wikipedia.org/wiki/Bootstrapping_(statistics)">bootstrap</a>
1019 to provide confidence intervals on our estimates. The
1020 bootstrap-derived upper and lower bounds on estimates let you see
1021 how accurate we believe those estimates to be. (Hover the mouse
1022 over the table headers to see the confidence levels.)</p>
1024 <p>A noisy benchmarking environment can cause some or many
1025 measurements to fall far from the mean. These outlying
1026 measurements can have a significant inflationary effect on the
1027 estimate of the standard deviation. We calculate and display an
1028 estimate of the extent to which the standard deviation has been
1029 inflated by outliers.</p>
1031<script type="text/javascript">
1033 function mangulate(rpt) {
1034 var measured = function(key) {
1035 var idx = rpt.reportKeys.indexOf(key);
1036 return rpt.reportMeasured.map(function(r) { return r[idx]; });
1038 var lowerBound = function(est) {
1039 return est.estPoint - est.estError.confIntLDX;
1041 var upperBound = function(est) {
1042 return est.estPoint + est.estError.confIntUDX;
1044 var number = rpt.reportNumber;
1045 var name = rpt.reportName;
1046 var mean = rpt.reportAnalysis.anMean.estPoint;
1047 var iters = measured("iters");
1048 var times = measured("time");
1049 var kdetimes = rpt.reportKDEs[0].kdeValues;
1050 var kdepdf = rpt.reportKDEs[0].kdePDF;
1052 var meanSecs = mean;
1053 var units = $.timeUnits(mean);
1054 var rgrs = rpt.reportAnalysis.anRegress[0];
1055 var scale = units[0];
1056 var olsTime = rgrs.regCoeffs.iters;
1057 $(".olstimept" + number).text(function() {
1058 return $.renderTime(olsTime.estPoint);
1060 $(".olstimelb" + number).text(function() {
1061 return $.renderTime(lowerBound(olsTime));
1063 $(".olstimeub" + number).text(function() {
1064 return $.renderTime(upperBound(olsTime));
1066 $(".olsr2pt" + number).text(function() {
1067 return rgrs.regRSquare.estPoint.toFixed(3);
1069 $(".olsr2lb" + number).text(function() {
1070 return lowerBound(rgrs.regRSquare).toFixed(3);
1072 $(".olsr2ub" + number).text(function() {
1073 return upperBound(rgrs.regRSquare).toFixed(3);
1076 kdetimes = $.scaleBy(scale, kdetimes);
1077 var kq = $("#kde" + number);
1079 [{ label: name + " time densities",
1080 data: $.zip(kdetimes, kdepdf),
1082 { xaxis: { tickFormatter: $.unitFormatter(scale) },
1083 yaxis: { ticks: false },
1084 grid: { borderColor: "#777",
1085 hoverable: true, markings: [ { color: '#6fd3fb',
1086 lineWidth: 1.5, xaxis: { from: mean, to: mean } } ] },
1088 var o = k.pointOffset({ x: mean, y: 0});
1089 kq.append('<div class="meanlegend" title="' + $.renderTime(meanSecs) +
1090 '" style="position:absolute;left:' + (o.left + 4) +
1091 'px;bottom:139px;">mean</div>');
1092 $.addTooltip("#kde" + number,
1093 function(secs) { return $.renderTime(secs / scale); });
1094 var timepairs = new Array(times.length);
1095 var lastiter = iters[iters.length-1];
1096 var olspairs = [[0,0], [lastiter, lastiter * scale * olsTime.estPoint]];
1097 for (var i = 0; i < times.length; i++)
1098 timepairs[i] = [iters[i],times[i]*scale];
1099 iterFormatter = function() {
1101 return function(iters) {
1105 return (iters / denom).toFixed();
1108 denom = '1e9'; power = '⁹';
1111 denom = '1e6'; power = '⁶';
1113 else if (iters >= 1e3) {
1114 denom = '1e3'; power = '³';
1118 iters = (iters / denom).toFixed();
1119 iters += '×10' + power + ' iters';
1126 $.plot($("#time" + number),
1127 [{ label: "regression", data: olspairs,
1128 lines: { show: true } },
1129 { label: name + " times", data: timepairs,
1130 points: { show: true } }],
1131 { grid: { borderColor: "#777", hoverable: true },
1132 xaxis: { tickFormatter: iterFormatter() },
1133 yaxis: { tickFormatter: $.unitFormatter(scale) } });
1134 $.addTooltip("#time" + number,
1135 function(iters,secs) {
1136 return ($.renderTime(secs / scale) + ' / ' +
1137 iters.toLocaleString() + ' iters');
1140 var cyclepairs = new Array(cycles.length);
1141 for (var i = 0; i < cycles.length; i++)
1142 cyclepairs[i] = [cycles[i],i];
1143 $.plot($("#cycle" + number),
1144 [{ label: name + " cycles",
1145 data: cyclepairs }],
1146 { points: { show: true },
1147 grid: { borderColor: "#777", hoverable: true },
1148 xaxis: { tickFormatter:
1149 function(cycles,axis) { return cycles + ' cycles'; }},
1150 yaxis: { ticks: false },
1152 $.addTooltip("#cycles" + number, function(x,y) { return x + ' cycles'; });
1155 var reports = 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10x1000","reportOutliers":{"highSevere":0,"highMild":0,"lowMild":0,"samplesSeen":42,"lowSevere":0},"reportMeasured":[[1.8466379988240078e-3,7.274569999964342e-4,3507547,1,null,null,null,null,null,null,null],[3.749734998564236e-3,1.2929680000013377e-3,7124480,2,null,null,null,null,null,null,null],[4.955500997311901e-3,1.607750000005126e-3,9415768,3,null,null,null,null,null,null,null],[6.903895002324134e-3,2.134022999996432e-3,13116844,4,null,null,null,null,null,null,null],[7.662063995667268e-3,2.6392199999918375e-3,14557279,5,null,null,null,null,null,null,null],[9.14627699967241e-3,3.1539210000062212e-3,17377777,6,null,null,null,null,null,null,null],[1.0610110002744477e-2,3.6542269999984e-3,20159943,7,null,null,null,null,null,null,null],[1.342409599601524e-2,4.194209999994314e-3,25506664,8,null,null,null,null,null,null,null],[1.5978417002770584e-2,4.999694999995086e-3,30365722,9,null,null,null,null,null,null,null],[1.627196399931563e-2,5.0767970000009655e-3,30906348,10,null,null,null,null,null,null,null],[1.8321754003409296e-2,6.067248999997332e-3,34814408,11,null,null,null,null,null,null,null],[1.8981221997819375e-2,6.094036999996888e-3,36053167,12,null,null,null,null,null,null,null],[1.9602098000177648e-2,6.6276209999927005e-3,37243869,13,null,null,null,null,null,null,null],[2.327671800594544e-2,7.2572819999976446e-3,44224234,14,null,null,null,null,null,null,null],[2.4468238996632863e-2,7.5967469999937975e-3,46488668,15,null,null,null,null,null,null,null],[2.6962415999150835e-2,8.19859499999609e-3,51228227,16,null,null,null,null,null,null,null],[2.748465700278757e-2,8.59233600000664e-3,52221056,17,null,null,null,null,null,null,null],[2.8489768999861553e-2,9.275811999998496e-3,54130289,18,null,null,null,null,null,null,null],[3.1534058005490806e-2,9.509088000001498e-3,59914520,19,null,null,null,null,null,null,null],[3.3379975000570994e-2,1.0019051000000445e-2,63421768,20,null,null,null,null,null,null,null],[3.21486390021164e-2,1.0772243000005233e-2,61082639,21,null,null,null,null,null,null,null],[3.4810391000064556e-2,1.1085932000000298e-2,66140774,22,null,null,null,null,null,null,null],[3.674557199701667e-2,1.138365399999941e-2,69816213,23,null,null,null,null,null,null,null],[4.020284400030505e-2,1.2410384999995472e-2,76374480,25,null,null,null,null,null,null,null],[4.22221479966538e-2,1.307710599999723e-2,80222608,26,null,null,null,null,null,null,null],[4.329287900327472e-2,1.3273174999994808e-2,82256995,27,null,null,null,null,null,null,null],[4.476459800207522e-2,1.390862699999218e-2,85052355,28,null,null,null,null,null,null,null],[4.4805204997828696e-2,1.4815548999990824e-2,85129419,30,null,null,null,null,null,null,null],[4.578064000088489e-2,1.531027100000415e-2,86983684,31,null,null,null,null,null,null,null],[5.494126000121469e-2,1.6736143000002812e-2,104386904,33,null,null,null,null,null,null,null],[5.856170300103258e-2,1.7582637999993267e-2,111266691,35,null,null,null,null,null,null,null],[5.556844300008379e-2,1.8157706000010876e-2,105579654,36,null,null,null,null,null,null,null],[5.72225259966217e-2,1.899279000001286e-2,108722481,38,null,null,null,null,null,null,null],[5.996240700187627e-2,2.0061890999997445e-2,113928361,40,null,null,null,null,null,null,null],[6.269381900347071e-2,2.0921506999997064e-2,119117391,42,null,null,null,null,null,null,null],[6.556441300199367e-2,2.1952963000003933e-2,124571593,44,null,null,null,null,null,null,null],[7.594630800304003e-2,2.3481523999990372e-2,144278413,47,null,null,null,null,null,null,null],[8.108867700502742e-2,2.4290333999999802e-2,154067861,49,null,null,null,null,null,null,null],[8.529218500189018e-2,2.583146699998906e-2,162048520,52,null,null,null,null,null,null,null],[8.946721100073773e-2,2.7083283999999708e-2,169988974,54,null,null,null,null,null,null,null],[9.48865440004738e-2,2.821290299999646e-2,180284176,57,null,null,null,null,null,null,null],[9.93107329995837e-2,2.9940874999994094e-2,188691292,60,null,null,null,null,null,null,null],[0.10492485300346743,3.141047500000127e-2,199358070,63,null,null,null,null,null,null,null],[0.10985853100282839,3.322559500000466e-2,208730998,66,null,null,null,null,null,null,null],[0.11500390100263758,3.45653819999967e-2,218507729,69,null,null,null,null,null,null,null],[0.12155503199755913,3.6797472000003495e-2,230955873,73,null,null,null,null,null,null,null],[0.11707255199871724,3.8233618000006686e-2,222432487,76,null,null,null,null,null,null,null],[0.12409541200031526,4.020329300000469e-2,235780319,80,null,null,null,null,null,null,null],[0.1282499109947821,4.197388699999749e-2,243674964,84,null,null,null,null,null,null,null],[0.1427478890036582,4.436167599999408e-2,271221846,89,null,null,null,null,null,null,null],[0.15047681400028523,4.6265106000006995e-2,285906018,93,null,null,null,null,null,null,null],[0.1592981859939755,4.917918000001009e-2,302666382,98,null,null,null,null,null,null,null],[0.16632080000272254,5.1931293999999184e-2,316009119,103,null,null,null,null,null,null,null],[0.1761690359999193,5.399041799999793e-2,334720023,108,null,null,null,null,null,null,null],[0.18700433299818542,5.68943619999942e-2,355308629,113,null,null,null,null,null,null,null],[0.19666026499908185,5.982605599999147e-2,373655007,119,null,null,null,null,null,null,null],[0.19374608200450893,6.273283400000196e-2,368117847,125,null,null,null,null,null,null,null],[0.18881391399918357,6.604477399999098e-2,358746101,131,null,null,null,null,null,null,null],[0.20665175699832616,6.900736000000052e-2,392637938,138,null,null,null,null,null,null,null],[0.2148972130016773,7.214676499999939e-2,408311504,144,null,null,null,null,null,null,null]]}];
1156 reports.map(mangulate);
1158 var benches = ["create/relation 10x100","create/relation 10x1000","create/relation 10x10000","restrict/restrict relation 10x10000 to 10x1","restrict/restrict relation 10x10000 to 10x5000","project/project 10x1000 to 1x1000","project/project 10x1000 to 5x1000","union/union identical 10x1000","union/union identical 10x10000","join/join identical 10x100","group/group 10x100","write/write 10x1000",];
1159 var ylabels = [[-0,'<a href="#b0">create/relation 10x100</a>'],[-1,'<a href="#b1">create/relation 10x1000</a>'],[-2,'<a href="#b2">create/relation 10x10000</a>'],[-3,'<a href="#b3">restrict/restrict relation 10x10000 to 10x1</a>'],[-4,'<a href="#b4">restrict/restrict relation 10x10000 to 10x5000</a>'],[-5,'<a href="#b5">project/project 10x1000 to 1x1000</a>'],[-6,'<a href="#b6">project/project 10x1000 to 5x1000</a>'],[-7,'<a href="#b7">union/union identical 10x1000</a>'],[-8,'<a href="#b8">union/union identical 10x10000</a>'],[-9,'<a href="#b9">join/join identical 10x100</a>'],[-10,'<a href="#b10">group/group 10x100</a>'],[-11,'<a href="#b11">write/write 10x1000</a>'],];
1160 var means = $.scaleTimes([5.906283388143756e-4,5.770838309782314e-3,5.784195729887314e-2,1.448797309139284e-2,8.447258684075218e-2,1.243577405705988e-2,1.547382196820991e-2,4.923173514046889e-2,0.5157808375258943,0.3374978199984254,4.70336206675725e-2,1.5673276303702817e-3,]);
1161 var stddevs = [4.502571301832575e-6,2.8815215559732134e-5,1.7121491767487851e-4,4.774249175945004e-4,1.4013810341920257e-3,4.600204612133745e-4,3.978143402171812e-4,3.275115670368287e-4,1.1935851498586624e-3,1.6965376723717264e-3,7.389605929837668e-4,6.970009193368111e-5,];
1162 stddevs = $.scaleBy(means[1], stddevs);
1165 var colors = ["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"];
1167 for (var i = 0; i < means[0].length; i++) {
1168 var name = benches[i].split(/\//);
1170 name = name.join('/');
1172 var color = colors[xs.length / 2 % colors.length];
1173 xs.push({ data: [[means[0][i], -i]], label: name, color: color,
1174 bars: { show: true, horizontal: true, barWidth: 0.75,
1175 align: "center" } });
1176 xs.push({ data: [[means[0][i], -i, stddevs[i]]], color: color,
1177 lines: { show: false },
1178 points: { radius: 0, errorbars: "x",
1179 xerr: { show: true, radius: 5,
1180 lowerCap: "-", upperCap: "-" } } });
1183 xs[xs.length - 2].data.push([means[0][i], -i]);
1184 xs[xs.length - 1].data.push([means[0][i], -i, stddevs[i]]);
1186 max_time = Math.max(max_time, means[0][i] + stddevs[i]);
1188 var oq = $("#overview");
1189 o = $.plot(oq, xs, { grid: { borderColor: "#777", hoverable: true },
1190 legend: { show: xs.length > 1 },
1191 xaxis: { max: max_time * 1.02 },
1192 yaxis: { ticks: ylabels, tickColor: '#ffffff' },
1193 zoom: { interactive: true, amount: 1.05 },
1194 pan: { interactive: true } });
1195 if (benches.length > 3)
1196 o.getPlaceholder().height(28*benches.length);
1200 $.addTooltip("#overview", function(x,y) { return $.renderTime(x / means[1]); });
1202$(document).ready(function () {
1203 $(".time").text(function(_, text) {
1204 return $.renderTime(text);
1206 $(".citime").text(function(_, text) {
1207 return $.renderTime(text);
1209 $(".percent").text(function(_, text) {
1210 return (text*100).toFixed(1);
1219 <div class="footfirst">
1221 <p>This report was created using the
1222 <a href="http://hackage.haskell.org/package/criterion">criterion</a>
1223 benchmark execution and performance analysis tool.</p>
1224 <p>Criterion is developed and maintained
1225 by <a href="http://www.serpentine.com/blog/">Bryan O'Sullivan</a>.</p>
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