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Delete report-lazy.html
commit1c212cf9f22245cd07dea8ef8ae71d9005d86a6e authorArmando Santos <armandoifsantos@gmail.com> dateWed 19 Feb 2020, 11:10 (7 years ago) parent1dab2f7
1 file changed−1169
report-lazy.html−1169
report-lazy.htmldeleted−1169
@@ -1,1169 +0,0 @@
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3− <head>
4− <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
5− <title>criterion report</title>
6− <script language="javascript" type="text/javascript">
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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;
19−if(yrange.from==null)yrange.from=yrange.axis.min;if(yrange.to==null)yrange.to=yrange.axis.max;if(xrange.to<xrange.axis.min||xrange.from>xrange.axis.max||yrange.to<yrange.axis.min||yrange.from>yrange.axis.max)continue;xrange.from=Math.max(xrange.from,xrange.axis.min);xrange.to=Math.min(xrange.to,xrange.axis.max);yrange.from=Math.max(yrange.from,yrange.axis.min);yrange.to=Math.min(yrange.to,yrange.axis.max);var xequal=xrange.from===xrange.to,yequal=yrange.from===yrange.to;if(xequal&&yequal){continue}xrange.from=Math.floor(xrange.axis.p2c(xrange.from));xrange.to=Math.floor(xrange.axis.p2c(xrange.to));yrange.from=Math.floor(yrange.axis.p2c(yrange.from));yrange.to=Math.floor(yrange.axis.p2c(yrange.to));if(xequal||yequal){var lineWidth=m.lineWidth||options.grid.markingsLineWidth,subPixel=lineWidth%2?.5:0;ctx.beginPath();ctx.strokeStyle=m.color||options.grid.markingsColor;ctx.lineWidth=lineWidth;if(xequal){ctx.moveTo(xrange.to+subPixel,yrange.from);ctx.lineTo(xrange.to+subPixel,yrange.to)}else{ctx.moveTo(xrange.from,yrange.to+subPixel);ctx.lineTo(xrange.to,yrange.to+subPixel)}ctx.stroke()}else{ctx.fillStyle=m.color||options.grid.markingsColor;ctx.fillRect(xrange.from,yrange.to,xrange.to-xrange.from,yrange.from-yrange.to)}}}axes=allAxes();bw=options.grid.borderWidth;for(var j=0;j<axes.length;++j){var axis=axes[j],box=axis.box,t=axis.tickLength,x,y,xoff,yoff;if(!axis.show||axis.ticks.length==0)continue;ctx.lineWidth=1;if(axis.direction=="x"){x=0;if(t=="full")y=axis.position=="top"?0:plotHeight;else y=box.top-plotOffset.top+(axis.position=="top"?box.height:0)}else{y=0;if(t=="full")x=axis.position=="left"?0:plotWidth;else x=box.left-plotOffset.left+(axis.position=="left"?box.width:0)}if(!axis.innermost){ctx.strokeStyle=axis.options.color;ctx.beginPath();xoff=yoff=0;if(axis.direction=="x")xoff=plotWidth+1;else yoff=plotHeight+1;if(ctx.lineWidth==1){if(axis.direction=="x"){y=Math.floor(y)+.5}else{x=Math.floor(x)+.5}}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff);ctx.stroke()}ctx.strokeStyle=axis.options.tickColor;ctx.beginPath();for(i=0;i<axis.ticks.length;++i){var v=axis.ticks[i].v;xoff=yoff=0;if(isNaN(v)||v<axis.min||v>axis.max||t=="full"&&(typeof bw=="object"&&bw[axis.position]>0||bw>0)&&(v==axis.min||v==axis.max))continue;if(axis.direction=="x"){x=axis.p2c(v);yoff=t=="full"?-plotHeight:t;if(axis.position=="top")yoff=-yoff}else{y=axis.p2c(v);xoff=t=="full"?-plotWidth:t;if(axis.position=="left")xoff=-xoff}if(ctx.lineWidth==1){if(axis.direction=="x")x=Math.floor(x)+.5;else y=Math.floor(y)+.5}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff)}ctx.stroke()}if(bw){bc=options.grid.borderColor;if(typeof bw=="object"||typeof bc=="object"){if(typeof bw!=="object"){bw={top:bw,right:bw,bottom:bw,left:bw}}if(typeof bc!=="object"){bc={top:bc,right:bc,bottom:bc,left:bc}}if(bw.top>0){ctx.strokeStyle=bc.top;ctx.lineWidth=bw.top;ctx.beginPath();ctx.moveTo(0-bw.left,0-bw.top/2);ctx.lineTo(plotWidth,0-bw.top/2);ctx.stroke()}if(bw.right>0){ctx.strokeStyle=bc.right;ctx.lineWidth=bw.right;ctx.beginPath();ctx.moveTo(plotWidth+bw.right/2,0-bw.top);ctx.lineTo(plotWidth+bw.right/2,plotHeight);ctx.stroke()}if(bw.bottom>0){ctx.strokeStyle=bc.bottom;ctx.lineWidth=bw.bottom;ctx.beginPath();ctx.moveTo(plotWidth+bw.right,plotHeight+bw.bottom/2);ctx.lineTo(0,plotHeight+bw.bottom/2);ctx.stroke()}if(bw.left>0){ctx.strokeStyle=bc.left;ctx.lineWidth=bw.left;ctx.beginPath();ctx.moveTo(0-bw.left/2,plotHeight+bw.bottom);ctx.lineTo(0-bw.left/2,0);ctx.stroke()}}else{ctx.lineWidth=bw;ctx.strokeStyle=options.grid.borderColor;ctx.strokeRect(-bw/2,-bw/2,plotWidth+bw,plotHeight+bw)}}ctx.restore()}function drawAxisLabels(){$.each(allAxes(),function(_,axis){var box=axis.box,legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=axis.options.font||"flot-tick-label tickLabel",tick,x,y,halign,valign;surface.removeText(layer);if(!axis.show||axis.ticks.length==0)return;for(var i=0;i<axis.ticks.length;++i){tick=axis.ticks[i];if(!tick.label||tick.v<axis.min||tick.v>axis.max)continue;if(axis.direction=="x"){halign="center";x=plotOffset.left+axis.p2c(tick.v);if(axis.position=="bottom"){y=box.top+box.padding}else{y=box.top+box.height-box.padding;valign="bottom"}}else{valign="middle";y=plotOffset.top+axis.p2c(tick.v);if(axis.position=="left"){x=box.left+box.width-box.padding;halign="right"}else{x=box.left+box.padding}}surface.addText(layer,x,y,tick.label,font,null,null,halign,valign)}})}function drawSeries(series){if(series.lines.show)drawSeriesLines(series);if(series.bars.show)drawSeriesBars(series);if(series.points.show)drawSeriesPoints(series)}function drawSeriesLines(series){function plotLine(datapoints,xoffset,yoffset,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,prevx=null,prevy=null;ctx.beginPath();for(var i=ps;i<points.length;i+=ps){var x1=points[i-ps],y1=points[i-ps+1],x2=points[i],y2=points[i+1];if(x1==null||x2==null)continue;if(y1<=y2&&y1<axisy.min){if(y2<axisy.min)continue;x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min){if(y1<axisy.min)continue;x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max){if(y2>axisy.max)continue;x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max){if(y1>axisy.max)continue;x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(x1!=prevx||y1!=prevy)ctx.moveTo(axisx.p2c(x1)+xoffset,axisy.p2c(y1)+yoffset);prevx=x2;prevy=y2;ctx.lineTo(axisx.p2c(x2)+xoffset,axisy.p2c(y2)+yoffset)}ctx.stroke()}function plotLineArea(datapoints,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,bottom=Math.min(Math.max(0,axisy.min),axisy.max),i=0,top,areaOpen=false,ypos=1,segmentStart=0,segmentEnd=0;while(true){if(ps>0&&i>points.length+ps)break;i+=ps;var x1=points[i-ps],y1=points[i-ps+ypos],x2=points[i],y2=points[i+ypos];if(areaOpen){if(ps>0&&x1!=null&&x2==null){segmentEnd=i;ps=-ps;ypos=2;continue}if(ps<0&&i==segmentStart+ps){ctx.fill();areaOpen=false;ps=-ps;ypos=1;i=segmentStart=segmentEnd+ps;continue}}if(x1==null||x2==null)continue;if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(!areaOpen){ctx.beginPath();ctx.moveTo(axisx.p2c(x1),axisy.p2c(bottom));areaOpen=true}if(y1>=axisy.max&&y2>=axisy.max){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.max));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.max));continue}else if(y1<=axisy.min&&y2<=axisy.min){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.min));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.min));continue}var x1old=x1,x2old=x2;if(y1<=y2&&y1<axisy.min&&y2>=axisy.min){x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min&&y1>=axisy.min){x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max&&y2<=axisy.max){x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max&&y1<=axisy.max){x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1!=x1old){ctx.lineTo(axisx.p2c(x1old),axisy.p2c(y1))}ctx.lineTo(axisx.p2c(x1),axisy.p2c(y1));ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));if(x2!=x2old){ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));ctx.lineTo(axisx.p2c(x2old),axisy.p2c(y2))}}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineJoin="round";var lw=series.lines.lineWidth,sw=series.shadowSize;if(lw>0&&sw>0){ctx.lineWidth=sw;ctx.strokeStyle="rgba(0,0,0,0.1)";var angle=Math.PI/18;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/2),Math.cos(angle)*(lw/2+sw/2),series.xaxis,series.yaxis);ctx.lineWidth=sw/2;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/4),Math.cos(angle)*(lw/2+sw/4),series.xaxis,series.yaxis)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;var fillStyle=getFillStyle(series.lines,series.color,0,plotHeight);if(fillStyle){ctx.fillStyle=fillStyle;plotLineArea(series.datapoints,series.xaxis,series.yaxis)}if(lw>0)plotLine(series.datapoints,0,0,series.xaxis,series.yaxis);ctx.restore()}function drawSeriesPoints(series){function plotPoints(datapoints,radius,fillStyle,offset,shadow,axisx,axisy,symbol){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){var x=points[i],y=points[i+1];if(x==null||x<axisx.min||x>axisx.max||y<axisy.min||y>axisy.max)continue;ctx.beginPath();x=axisx.p2c(x);y=axisy.p2c(y)+offset;if(symbol=="circle")ctx.arc(x,y,radius,0,shadow?Math.PI:Math.PI*2,false);else symbol(ctx,x,y,radius,shadow);ctx.closePath();if(fillStyle){ctx.fillStyle=fillStyle;ctx.fill()}ctx.stroke()}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var lw=series.points.lineWidth,sw=series.shadowSize,radius=series.points.radius,symbol=series.points.symbol;if(lw==0)lw=1e-4;if(lw>0&&sw>0){var w=sw/2;ctx.lineWidth=w;ctx.strokeStyle="rgba(0,0,0,0.1)";plotPoints(series.datapoints,radius,null,w+w/2,true,series.xaxis,series.yaxis,symbol);ctx.strokeStyle="rgba(0,0,0,0.2)";plotPoints(series.datapoints,radius,null,w/2,true,series.xaxis,series.yaxis,symbol)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;plotPoints(series.datapoints,radius,getFillStyle(series.points,series.color),0,false,series.xaxis,series.yaxis,symbol);ctx.restore()}function drawBar(x,y,b,barLeft,barRight,fillStyleCallback,axisx,axisy,c,horizontal,lineWidth){var left,right,bottom,top,drawLeft,drawRight,drawTop,drawBottom,tmp;if(horizontal){drawBottom=drawRight=drawTop=true;drawLeft=false;left=b;right=x;top=y+barLeft;bottom=y+barRight;if(right<left){tmp=right;right=left;left=tmp;drawLeft=true;drawRight=false}}else{drawLeft=drawRight=drawTop=true;drawBottom=false;left=x+barLeft;right=x+barRight;bottom=b;top=y;if(top<bottom){tmp=top;top=bottom;bottom=tmp;drawBottom=true;drawTop=false}}if(right<axisx.min||left>axisx.max||top<axisy.min||bottom>axisy.max)return;if(left<axisx.min){left=axisx.min;drawLeft=false}if(right>axisx.max){right=axisx.max;drawRight=false}if(bottom<axisy.min){bottom=axisy.min;drawBottom=false}if(top>axisy.max){top=axisy.max;drawTop=false}left=axisx.p2c(left);bottom=axisy.p2c(bottom);right=axisx.p2c(right);top=axisy.p2c(top);if(fillStyleCallback){c.fillStyle=fillStyleCallback(bottom,top);c.fillRect(left,top,right-left,bottom-top)}if(lineWidth>0&&(drawLeft||drawRight||drawTop||drawBottom)){c.beginPath();c.moveTo(left,bottom);if(drawLeft)c.lineTo(left,top);else c.moveTo(left,top);if(drawTop)c.lineTo(right,top);else c.moveTo(right,top);if(drawRight)c.lineTo(right,bottom);else c.moveTo(right,bottom);if(drawBottom)c.lineTo(left,bottom);else c.moveTo(left,bottom);c.stroke()}}function drawSeriesBars(series){function plotBars(datapoints,barLeft,barRight,fillStyleCallback,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){if(points[i]==null)continue;drawBar(points[i],points[i+1],points[i+2],barLeft,barRight,fillStyleCallback,axisx,axisy,ctx,series.bars.horizontal,series.bars.lineWidth)}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineWidth=series.bars.lineWidth;ctx.strokeStyle=series.color;var barLeft;switch(series.bars.align){case"left":barLeft=0;break;case"right":barLeft=-series.bars.barWidth;break;default:barLeft=-series.bars.barWidth/2}var fillStyleCallback=series.bars.fill?function(bottom,top){return 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($.isFunction(options.legend.sorted)){entries.sort(options.legend.sorted)}else 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20− </script>
21− <script language="javascript" type="text/javascript">
22− /* Javascript plotting library for jQuery, version 0.8.3.
23−
24−Copyright (c) 2007-2014 IOLA and Ole Laursen.
25−Licensed under the MIT license.
26−
27−*/
28−(function($){var options={series:{points:{errorbars:null,xerr:{err:"x",show:null,asymmetric:null,upperCap:null,lowerCap:null,color:null,radius:null},yerr:{err:"y",show:null,asymmetric:null,upperCap:null,lowerCap:null,color:null,radius:null}}}};function processRawData(plot,series,data,datapoints){if(!series.points.errorbars)return;var format=[{x:true,number:true,required:true},{y:true,number:true,required:true}];var errors=series.points.errorbars;if(errors=="x"||errors=="xy"){if(series.points.xerr.asymmetric){format.push({x:true,number:true,required:true});format.push({x:true,number:true,required:true})}else format.push({x:true,number:true,required:true})}if(errors=="y"||errors=="xy"){if(series.points.yerr.asymmetric){format.push({y:true,number:true,required:true});format.push({y:true,number:true,required:true})}else format.push({y:true,number:true,required:true})}datapoints.format=format}function parseErrors(series,i){var points=series.datapoints.points;var exl=null,exu=null,eyl=null,eyu=null;var xerr=series.points.xerr,yerr=series.points.yerr;var eb=series.points.errorbars;if(eb=="x"||eb=="xy"){if(xerr.asymmetric){exl=points[i+2];exu=points[i+3];if(eb=="xy")if(yerr.asymmetric){eyl=points[i+4];eyu=points[i+5]}else eyl=points[i+4]}else{exl=points[i+2];if(eb=="xy")if(yerr.asymmetric){eyl=points[i+3];eyu=points[i+4]}else eyl=points[i+3]}}else if(eb=="y")if(yerr.asymmetric){eyl=points[i+2];eyu=points[i+3]}else eyl=points[i+2];if(exu==null)exu=exl;if(eyu==null)eyu=eyl;var errRanges=[exl,exu,eyl,eyu];if(!xerr.show){errRanges[0]=null;errRanges[1]=null}if(!yerr.show){errRanges[2]=null;errRanges[3]=null}return errRanges}function drawSeriesErrors(plot,ctx,s){var points=s.datapoints.points,ps=s.datapoints.pointsize,ax=[s.xaxis,s.yaxis],radius=s.points.radius,err=[s.points.xerr,s.points.yerr];var invertX=false;if(ax[0].p2c(ax[0].max)<ax[0].p2c(ax[0].min)){invertX=true;var tmp=err[0].lowerCap;err[0].lowerCap=err[0].upperCap;err[0].upperCap=tmp}var invertY=false;if(ax[1].p2c(ax[1].min)<ax[1].p2c(ax[1].max)){invertY=true;var tmp=err[1].lowerCap;err[1].lowerCap=err[1].upperCap;err[1].upperCap=tmp}for(var i=0;i<s.datapoints.points.length;i+=ps){var errRanges=parseErrors(s,i);for(var e=0;e<err.length;e++){var minmax=[ax[e].min,ax[e].max];if(errRanges[e*err.length]){var x=points[i],y=points[i+1];var upper=[x,y][e]+errRanges[e*err.length+1],lower=[x,y][e]-errRanges[e*err.length];if(err[e].err=="x")if(y>ax[1].max||y<ax[1].min||upper<ax[0].min||lower>ax[0].max)continue;if(err[e].err=="y")if(x>ax[0].max||x<ax[0].min||upper<ax[1].min||lower>ax[1].max)continue;var drawUpper=true,drawLower=true;if(upper>minmax[1]){drawUpper=false;upper=minmax[1]}if(lower<minmax[0]){drawLower=false;lower=minmax[0]}if(err[e].err=="x"&&invertX||err[e].err=="y"&&invertY){var tmp=lower;lower=upper;upper=tmp;tmp=drawLower;drawLower=drawUpper;drawUpper=tmp;tmp=minmax[0];minmax[0]=minmax[1];minmax[1]=tmp}x=ax[0].p2c(x),y=ax[1].p2c(y),upper=ax[e].p2c(upper);lower=ax[e].p2c(lower);minmax[0]=ax[e].p2c(minmax[0]);minmax[1]=ax[e].p2c(minmax[1]);var lw=err[e].lineWidth?err[e].lineWidth:s.points.lineWidth,sw=s.points.shadowSize!=null?s.points.shadowSize:s.shadowSize;if(lw>0&&sw>0){var w=sw/2;ctx.lineWidth=w;ctx.strokeStyle="rgba(0,0,0,0.1)";drawError(ctx,err[e],x,y,upper,lower,drawUpper,drawLower,radius,w+w/2,minmax);ctx.strokeStyle="rgba(0,0,0,0.2)";drawError(ctx,err[e],x,y,upper,lower,drawUpper,drawLower,radius,w/2,minmax)}ctx.strokeStyle=err[e].color?err[e].color:s.color;ctx.lineWidth=lw;drawError(ctx,err[e],x,y,upper,lower,drawUpper,drawLower,radius,0,minmax)}}}}function 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drawPath(ctx,[[x-radius,lower],[x+radius,lower]])}else if($.isFunction(err.lowerCap)){if(err.err=="x")err.lowerCap(ctx,lower,y,radius);else err.lowerCap(ctx,x,lower,radius)}}}function drawPath(ctx,pts){ctx.beginPath();ctx.moveTo(pts[0][0],pts[0][1]);for(var p=1;p<pts.length;p++)ctx.lineTo(pts[p][0],pts[p][1]);ctx.stroke()}function draw(plot,ctx){var plotOffset=plot.getPlotOffset();ctx.save();ctx.translate(plotOffset.left,plotOffset.top);$.each(plot.getData(),function(i,s){if(s.points.errorbars&&(s.points.xerr.show||s.points.yerr.show))drawSeriesErrors(plot,ctx,s)});ctx.restore()}function init(plot){plot.hooks.processRawData.push(processRawData);plot.hooks.draw.push(draw)}$.plot.plugins.push({init:init,options:options,name:"errorbars",version:"1.0"})})(jQuery);
29− </script>
30− <script language="javascript" type="text/javascript">
31− /* Javascript plotting library for jQuery, version 0.8.3.
32−
33−Copyright (c) 2007-2014 IOLA and Ole Laursen.
34−Licensed under the MIT license.
35−
36−*/
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258− </head>
259− <body>
260− <div id="wrap">
261− <div id="main" class="body">
262− <h1>criterion performance measurements</h1>
263−
264−<h2>overview</h2>
265−
266−<p><a href="#grokularation">want to understand this report?</a></p>
267−
268−<div id="overview" class="ovchart" style="width:900px;height:100px;"></div>
269−
270−<h2><a name="b0">Matrix Composition/10x10/WHNF - 10x10</a></h2>
271− <table width="100%">
272− <tbody>
273− <tr>
274− <td><div id="kde0" class="kdechart"
275− style="width:450px;height:278px;"></div></td>
276− <td><div id="time0" class="timechart"
277− style="width:450px;height:278px;"></div></td>
278−<!--
279− <td><div id="cycle0" class="cyclechart"
280− style="width:300px;height:278px;"></div></td>
281−-->
282− </tr>
283− </tbody>
284− </table>
285−
286− <table>
287− <thead class="analysis">
288− <th></th>
289− <th class="cibound"
290− title="0.95 confidence level">lower bound</th>
291− <th>estimate</th>
292− <th class="cibound"
293− title="0.95 confidence level">upper bound</th>
294− </thead>
295− <tbody>
296− <tr>
297− <td>OLS regression</td>
298− <td><span class="confinterval olstimelb0">xxx</span></td>
299− <td><span class="olstimept0">xxx</span></td>
300− <td><span class="confinterval olstimeub0">xxx</span></td>
301− </tr>
302− <tr>
303− <td>R&#xb2; goodness-of-fit</td>
304− <td><span class="confinterval olsr2lb0">xxx</span></td>
305− <td><span class="olsr2pt0">xxx</span></td>
306− <td><span class="confinterval olsr2ub0">xxx</span></td>
307− </tr>
308− <tr>
309− <td>Mean execution time</td>
310− <td><span class="confinterval citime">2.429905310206746e-8</span></td>
311− <td><span class="time">2.4514866756029205e-8</span></td>
312− <td><span class="confinterval citime">2.48855951010762e-8</span></td>
313− </tr>
314− <tr>
315− <td>Standard deviation</td>
316− <td><span class="confinterval citime">6.998202238158643e-10</span></td>
317− <td><span class="time">9.647785488002723e-10</span></td>
318− <td><span class="confinterval citime">1.4224955576371737e-9</span></td>
319− </tr>
320− </tbody>
321− </table>
322−
323− <span class="outliers">
324− <p>Outlying measurements have severe
325− (<span class="percent">0.6233390798012226</span>%)
326− effect on estimated standard deviation.</p>
327− </span>
328−<h2><a name="b1">Matrix Composition/10x10/NF - 10x10</a></h2>
329− <table width="100%">
330− <tbody>
331− <tr>
332− <td><div id="kde1" class="kdechart"
333− style="width:450px;height:278px;"></div></td>
334− <td><div id="time1" class="timechart"
335− style="width:450px;height:278px;"></div></td>
336−<!--
337− <td><div id="cycle1" class="cyclechart"
338− style="width:300px;height:278px;"></div></td>
339−-->
340− </tr>
341− </tbody>
342− </table>
343−
344− <table>
345− <thead class="analysis">
346− <th></th>
347− <th class="cibound"
348− title="0.95 confidence level">lower bound</th>
349− <th>estimate</th>
350− <th class="cibound"
351− title="0.95 confidence level">upper bound</th>
352− </thead>
353− <tbody>
354− <tr>
355− <td>OLS regression</td>
356− <td><span class="confinterval olstimelb1">xxx</span></td>
357− <td><span class="olstimept1">xxx</span></td>
358− <td><span class="confinterval olstimeub1">xxx</span></td>
359− </tr>
360− <tr>
361− <td>R&#xb2; goodness-of-fit</td>
362− <td><span class="confinterval olsr2lb1">xxx</span></td>
363− <td><span class="olsr2pt1">xxx</span></td>
364− <td><span class="confinterval olsr2ub1">xxx</span></td>
365− </tr>
366− <tr>
367− <td>Mean execution time</td>
368− <td><span class="confinterval citime">2.0370099798754226e-4</span></td>
369− <td><span class="time">2.0678688880349948e-4</span></td>
370− <td><span class="confinterval citime">2.120795744857106e-4</span></td>
371− </tr>
372− <tr>
373− <td>Standard deviation</td>
374− <td><span class="confinterval citime">9.393316405757422e-6</span></td>
375− <td><span class="time">1.2679488723877765e-5</span></td>
376− <td><span class="confinterval citime">1.685081532022437e-5</span></td>
377− </tr>
378− </tbody>
379− </table>
380−
381− <span class="outliers">
382− <p>Outlying measurements have severe
383− (<span class="percent">0.5927893628180484</span>%)
384− effect on estimated standard deviation.</p>
385− </span>
386−<h2><a name="b2">Matrix Composition/100x100/WHNF - 100x100</a></h2>
387− <table width="100%">
388− <tbody>
389− <tr>
390− <td><div id="kde2" class="kdechart"
391− style="width:450px;height:278px;"></div></td>
392− <td><div id="time2" class="timechart"
393− style="width:450px;height:278px;"></div></td>
394−<!--
395− <td><div id="cycle2" class="cyclechart"
396− style="width:300px;height:278px;"></div></td>
397−-->
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399− </tbody>
400− </table>
401−
402− <table>
403− <thead class="analysis">
404− <th></th>
405− <th class="cibound"
406− title="0.95 confidence level">lower bound</th>
407− <th>estimate</th>
408− <th class="cibound"
409− title="0.95 confidence level">upper bound</th>
410− </thead>
411− <tbody>
412− <tr>
413− <td>OLS regression</td>
414− <td><span class="confinterval olstimelb2">xxx</span></td>
415− <td><span class="olstimept2">xxx</span></td>
416− <td><span class="confinterval olstimeub2">xxx</span></td>
417− </tr>
418− <tr>
419− <td>R&#xb2; goodness-of-fit</td>
420− <td><span class="confinterval olsr2lb2">xxx</span></td>
421− <td><span class="olsr2pt2">xxx</span></td>
422− <td><span class="confinterval olsr2ub2">xxx</span></td>
423− </tr>
424− <tr>
425− <td>Mean execution time</td>
426− <td><span class="confinterval citime">2.0050477013786835e-8</span></td>
427− <td><span class="time">2.032500748180748e-8</span></td>
428− <td><span class="confinterval citime">2.076846088643864e-8</span></td>
429− </tr>
430− <tr>
431− <td>Standard deviation</td>
432− <td><span class="confinterval citime">8.13204445187969e-10</span></td>
433− <td><span class="time">1.1748079988373767e-9</span></td>
434− <td><span class="confinterval citime">1.9440744217217704e-9</span></td>
435− </tr>
436− </tbody>
437− </table>
438−
439− <span class="outliers">
440− <p>Outlying measurements have severe
441− (<span class="percent">0.783687156722499</span>%)
442− effect on estimated standard deviation.</p>
443− </span>
444−<h2><a name="b3">Matrix Composition/100x100/NF - 100x100</a></h2>
445− <table width="100%">
446− <tbody>
447− <tr>
448− <td><div id="kde3" class="kdechart"
449− style="width:450px;height:278px;"></div></td>
450− <td><div id="time3" class="timechart"
451− style="width:450px;height:278px;"></div></td>
452−<!--
453− <td><div id="cycle3" class="cyclechart"
454− style="width:300px;height:278px;"></div></td>
455−-->
456− </tr>
457− </tbody>
458− </table>
459−
460− <table>
461− <thead class="analysis">
462− <th></th>
463− <th class="cibound"
464− title="0.95 confidence level">lower bound</th>
465− <th>estimate</th>
466− <th class="cibound"
467− title="0.95 confidence level">upper bound</th>
468− </thead>
469− <tbody>
470− <tr>
471− <td>OLS regression</td>
472− <td><span class="confinterval olstimelb3">xxx</span></td>
473− <td><span class="olstimept3">xxx</span></td>
474− <td><span class="confinterval olstimeub3">xxx</span></td>
475− </tr>
476− <tr>
477− <td>R&#xb2; goodness-of-fit</td>
478− <td><span class="confinterval olsr2lb3">xxx</span></td>
479− <td><span class="olsr2pt3">xxx</span></td>
480− <td><span class="confinterval olsr2ub3">xxx</span></td>
481− </tr>
482− <tr>
483− <td>Mean execution time</td>
484− <td><span class="confinterval citime">0.3498868480000965</span></td>
485− <td><span class="time">0.3562973993334708</span></td>
486− <td><span class="confinterval citime">0.36268595299998196</span></td>
487− </tr>
488− <tr>
489− <td>Standard deviation</td>
490− <td><span class="confinterval citime">3.523610261477639e-3</span></td>
491− <td><span class="time">7.81042655007339e-3</span></td>
492− <td><span class="confinterval citime">9.18947292446396e-3</span></td>
493− </tr>
494− </tbody>
495− </table>
496−
497− <span class="outliers">
498− <p>Outlying measurements have moderate
499− (<span class="percent">0.1875</span>%)
500− effect on estimated standard deviation.</p>
501− </span>
502−<h2><a name="b4">Matrix Composition/200x200/WHNF - 200x200</a></h2>
503− <table width="100%">
504− <tbody>
505− <tr>
506− <td><div id="kde4" class="kdechart"
507− style="width:450px;height:278px;"></div></td>
508− <td><div id="time4" class="timechart"
509− style="width:450px;height:278px;"></div></td>
510−<!--
511− <td><div id="cycle4" class="cyclechart"
512− style="width:300px;height:278px;"></div></td>
513−-->
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515− </tbody>
516− </table>
517−
518− <table>
519− <thead class="analysis">
520− <th></th>
521− <th class="cibound"
522− title="0.95 confidence level">lower bound</th>
523− <th>estimate</th>
524− <th class="cibound"
525− title="0.95 confidence level">upper bound</th>
526− </thead>
527− <tbody>
528− <tr>
529− <td>OLS regression</td>
530− <td><span class="confinterval olstimelb4">xxx</span></td>
531− <td><span class="olstimept4">xxx</span></td>
532− <td><span class="confinterval olstimeub4">xxx</span></td>
533− </tr>
534− <tr>
535− <td>R&#xb2; goodness-of-fit</td>
536− <td><span class="confinterval olsr2lb4">xxx</span></td>
537− <td><span class="olsr2pt4">xxx</span></td>
538− <td><span class="confinterval olsr2ub4">xxx</span></td>
539− </tr>
540− <tr>
541− <td>Mean execution time</td>
542− <td><span class="confinterval citime">2.0235240565823596e-8</span></td>
543− <td><span class="time">2.0667543937993357e-8</span></td>
544− <td><span class="confinterval citime">2.143971075518602e-8</span></td>
545− </tr>
546− <tr>
547− <td>Standard deviation</td>
548− <td><span class="confinterval citime">1.1209286317154773e-9</span></td>
549− <td><span class="time">1.8602778611455406e-9</span></td>
550− <td><span class="confinterval citime">2.8888629297529752e-9</span></td>
551− </tr>
552− </tbody>
553− </table>
554−
555− <span class="outliers">
556− <p>Outlying measurements have severe
557− (<span class="percent">0.8983666121212811</span>%)
558− effect on estimated standard deviation.</p>
559− </span>
560−<h2><a name="b5">Matrix Composition/200x200/NF - 200x200</a></h2>
561− <table width="100%">
562− <tbody>
563− <tr>
564− <td><div id="kde5" class="kdechart"
565− style="width:450px;height:278px;"></div></td>
566− <td><div id="time5" class="timechart"
567− style="width:450px;height:278px;"></div></td>
568−<!--
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570− style="width:300px;height:278px;"></div></td>
571−-->
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573− </tbody>
574− </table>
575−
576− <table>
577− <thead class="analysis">
578− <th></th>
579− <th class="cibound"
580− title="0.95 confidence level">lower bound</th>
581− <th>estimate</th>
582− <th class="cibound"
583− title="0.95 confidence level">upper bound</th>
584− </thead>
585− <tbody>
586− <tr>
587− <td>OLS regression</td>
588− <td><span class="confinterval olstimelb5">xxx</span></td>
589− <td><span class="olstimept5">xxx</span></td>
590− <td><span class="confinterval olstimeub5">xxx</span></td>
591− </tr>
592− <tr>
593− <td>R&#xb2; goodness-of-fit</td>
594− <td><span class="confinterval olsr2lb5">xxx</span></td>
595− <td><span class="olsr2pt5">xxx</span></td>
596− <td><span class="confinterval olsr2ub5">xxx</span></td>
597− </tr>
598− <tr>
599− <td>Mean execution time</td>
600− <td><span class="confinterval citime">4.562200145314364</span></td>
601− <td><span class="time">4.623436544688502</span></td>
602− <td><span class="confinterval citime">4.66968996875039</span></td>
603− </tr>
604− <tr>
605− <td>Standard deviation</td>
606− <td><span class="confinterval citime">3.460169931915702e-2</span></td>
607− <td><span class="time">6.412809734921064e-2</span></td>
608− <td><span class="confinterval citime">8.889305315202672e-2</span></td>
609− </tr>
610− </tbody>
611− </table>
612−
613− <span class="outliers">
614− <p>Outlying measurements have moderate
615− (<span class="percent">0.18749999999999997</span>%)
616− effect on estimated standard deviation.</p>
617− </span>
618−<h2><a name="b6">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/List Distribution - Applicative version</a></h2>
619− <table width="100%">
620− <tbody>
621− <tr>
622− <td><div id="kde6" class="kdechart"
623− style="width:450px;height:278px;"></div></td>
624− <td><div id="time6" class="timechart"
625− style="width:450px;height:278px;"></div></td>
626−<!--
627− <td><div id="cycle6" class="cyclechart"
628− style="width:300px;height:278px;"></div></td>
629−-->
630− </tr>
631− </tbody>
632− </table>
633−
634− <table>
635− <thead class="analysis">
636− <th></th>
637− <th class="cibound"
638− title="0.95 confidence level">lower bound</th>
639− <th>estimate</th>
640− <th class="cibound"
641− title="0.95 confidence level">upper bound</th>
642− </thead>
643− <tbody>
644− <tr>
645− <td>OLS regression</td>
646− <td><span class="confinterval olstimelb6">xxx</span></td>
647− <td><span class="olstimept6">xxx</span></td>
648− <td><span class="confinterval olstimeub6">xxx</span></td>
649− </tr>
650− <tr>
651− <td>R&#xb2; goodness-of-fit</td>
652− <td><span class="confinterval olsr2lb6">xxx</span></td>
653− <td><span class="olsr2pt6">xxx</span></td>
654− <td><span class="confinterval olsr2ub6">xxx</span></td>
655− </tr>
656− <tr>
657− <td>Mean execution time</td>
658− <td><span class="confinterval citime">2.2228158466256596</span></td>
659− <td><span class="time">2.3105826970010335</span></td>
660− <td><span class="confinterval citime">2.477541572751458</span></td>
661− </tr>
662− <tr>
663− <td>Standard deviation</td>
664− <td><span class="confinterval citime">1.668510235695031e-3</span></td>
665− <td><span class="time">0.16563995052316</span></td>
666− <td><span class="confinterval citime">0.19608795539767002</span></td>
667− </tr>
668− </tbody>
669− </table>
670−
671− <span class="outliers">
672− <p>Outlying measurements have moderate
673− (<span class="percent">0.2024935146554294</span>%)
674− effect on estimated standard deviation.</p>
675− </span>
676−<h2><a name="b7">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Applicative version</a></h2>
677− <table width="100%">
678− <tbody>
679− <tr>
680− <td><div id="kde7" class="kdechart"
681− style="width:450px;height:278px;"></div></td>
682− <td><div id="time7" class="timechart"
683− style="width:450px;height:278px;"></div></td>
684−<!--
685− <td><div id="cycle7" class="cyclechart"
686− style="width:300px;height:278px;"></div></td>
687−-->
688− </tr>
689− </tbody>
690− </table>
691−
692− <table>
693− <thead class="analysis">
694− <th></th>
695− <th class="cibound"
696− title="0.95 confidence level">lower bound</th>
697− <th>estimate</th>
698− <th class="cibound"
699− title="0.95 confidence level">upper bound</th>
700− </thead>
701− <tbody>
702− <tr>
703− <td>OLS regression</td>
704− <td><span class="confinterval olstimelb7">xxx</span></td>
705− <td><span class="olstimept7">xxx</span></td>
706− <td><span class="confinterval olstimeub7">xxx</span></td>
707− </tr>
708− <tr>
709− <td>R&#xb2; goodness-of-fit</td>
710− <td><span class="confinterval olsr2lb7">xxx</span></td>
711− <td><span class="olsr2pt7">xxx</span></td>
712− <td><span class="confinterval olsr2ub7">xxx</span></td>
713− </tr>
714− <tr>
715− <td>Mean execution time</td>
716− <td><span class="confinterval citime">2.4757740044307665e-3</span></td>
717− <td><span class="time">2.672449312433179e-3</span></td>
718− <td><span class="confinterval citime">3.145392355479385e-3</span></td>
719− </tr>
720− <tr>
721− <td>Standard deviation</td>
722− <td><span class="confinterval citime">4.61354238444537e-4</span></td>
723− <td><span class="time">9.064184723100453e-4</span></td>
724− <td><span class="confinterval citime">1.692293726555292e-3</span></td>
725− </tr>
726− </tbody>
727− </table>
728−
729− <span class="outliers">
730− <p>Outlying measurements have severe
731− (<span class="percent">0.9598911701243743</span>%)
732− effect on estimated standard deviation.</p>
733− </span>
734−<h2><a name="b8">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Selective version</a></h2>
735− <table width="100%">
736− <tbody>
737− <tr>
738− <td><div id="kde8" class="kdechart"
739− style="width:450px;height:278px;"></div></td>
740− <td><div id="time8" class="timechart"
741− style="width:450px;height:278px;"></div></td>
742−<!--
743− <td><div id="cycle8" class="cyclechart"
744− style="width:300px;height:278px;"></div></td>
745−-->
746− </tr>
747− </tbody>
748− </table>
749−
750− <table>
751− <thead class="analysis">
752− <th></th>
753− <th class="cibound"
754− title="0.95 confidence level">lower bound</th>
755− <th>estimate</th>
756− <th class="cibound"
757− title="0.95 confidence level">upper bound</th>
758− </thead>
759− <tbody>
760− <tr>
761− <td>OLS regression</td>
762− <td><span class="confinterval olstimelb8">xxx</span></td>
763− <td><span class="olstimept8">xxx</span></td>
764− <td><span class="confinterval olstimeub8">xxx</span></td>
765− </tr>
766− <tr>
767− <td>R&#xb2; goodness-of-fit</td>
768− <td><span class="confinterval olsr2lb8">xxx</span></td>
769− <td><span class="olsr2pt8">xxx</span></td>
770− <td><span class="confinterval olsr2ub8">xxx</span></td>
771− </tr>
772− <tr>
773− <td>Mean execution time</td>
774− <td><span class="confinterval citime">1.0094556598949091e-3</span></td>
775− <td><span class="time">1.0459672120901292e-3</span></td>
776− <td><span class="confinterval citime">1.1900093977774448e-3</span></td>
777− </tr>
778− <tr>
779− <td>Standard deviation</td>
780− <td><span class="confinterval citime">4.936441129145525e-5</span></td>
781− <td><span class="time">2.169293235834326e-4</span></td>
782− <td><span class="confinterval citime">4.929920714921142e-4</span></td>
783− </tr>
784− </tbody>
785− </table>
786−
787− <span class="outliers">
788− <p>Outlying measurements have severe
789− (<span class="percent">0.9240968489851639</span>%)
790− effect on estimated standard deviation.</p>
791− </span>
792−<h2><a name="b9">Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Applicative version</a></h2>
793− <table width="100%">
794− <tbody>
795− <tr>
796− <td><div id="kde9" class="kdechart"
797− style="width:450px;height:278px;"></div></td>
798− <td><div id="time9" class="timechart"
799− style="width:450px;height:278px;"></div></td>
800−<!--
801− <td><div id="cycle9" class="cyclechart"
802− style="width:300px;height:278px;"></div></td>
803−-->
804− </tr>
805− </tbody>
806− </table>
807−
808− <table>
809− <thead class="analysis">
810− <th></th>
811− <th class="cibound"
812− title="0.95 confidence level">lower bound</th>
813− <th>estimate</th>
814− <th class="cibound"
815− title="0.95 confidence level">upper bound</th>
816− </thead>
817− <tbody>
818− <tr>
819− <td>OLS regression</td>
820− <td><span class="confinterval olstimelb9">xxx</span></td>
821− <td><span class="olstimept9">xxx</span></td>
822− <td><span class="confinterval olstimeub9">xxx</span></td>
823− </tr>
824− <tr>
825− <td>R&#xb2; goodness-of-fit</td>
826− <td><span class="confinterval olsr2lb9">xxx</span></td>
827− <td><span class="olsr2pt9">xxx</span></td>
828− <td><span class="confinterval olsr2ub9">xxx</span></td>
829− </tr>
830− <tr>
831− <td>Mean execution time</td>
832− <td><span class="confinterval citime">1.1142126609993284</span></td>
833− <td><span class="time">1.1506916786456713</span></td>
834− <td><span class="confinterval citime">1.1642280000833125</span></td>
835− </tr>
836− <tr>
837− <td>Standard deviation</td>
838− <td><span class="confinterval citime">3.115381125098793e-3</span></td>
839− <td><span class="time">2.468474748870533e-2</span></td>
840− <td><span class="confinterval citime">3.126079530343771e-2</span></td>
841− </tr>
842− </tbody>
843− </table>
844−
845− <span class="outliers">
846− <p>Outlying measurements have moderate
847− (<span class="percent">0.1875</span>%)
848− effect on estimated standard deviation.</p>
849− </span>
850−<h2><a name="b10">Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Selective version</a></h2>
851− <table width="100%">
852− <tbody>
853− <tr>
854− <td><div id="kde10" class="kdechart"
855− style="width:450px;height:278px;"></div></td>
856− <td><div id="time10" class="timechart"
857− style="width:450px;height:278px;"></div></td>
858−<!--
859− <td><div id="cycle10" class="cyclechart"
860− style="width:300px;height:278px;"></div></td>
861−-->
862− </tr>
863− </tbody>
864− </table>
865−
866− <table>
867− <thead class="analysis">
868− <th></th>
869− <th class="cibound"
870− title="0.95 confidence level">lower bound</th>
871− <th>estimate</th>
872− <th class="cibound"
873− title="0.95 confidence level">upper bound</th>
874− </thead>
875− <tbody>
876− <tr>
877− <td>OLS regression</td>
878− <td><span class="confinterval olstimelb10">xxx</span></td>
879− <td><span class="olstimept10">xxx</span></td>
880− <td><span class="confinterval olstimeub10">xxx</span></td>
881− </tr>
882− <tr>
883− <td>R&#xb2; goodness-of-fit</td>
884− <td><span class="confinterval olsr2lb10">xxx</span></td>
885− <td><span class="olsr2pt10">xxx</span></td>
886− <td><span class="confinterval olsr2ub10">xxx</span></td>
887− </tr>
888− <tr>
889− <td>Mean execution time</td>
890− <td><span class="confinterval citime">0.530786815458517</span></td>
891− <td><span class="time">0.5400133454575856</span></td>
892− <td><span class="confinterval citime">0.5473636064980383</span></td>
893− </tr>
894− <tr>
895− <td>Standard deviation</td>
896− <td><span class="confinterval citime">4.333057553316104e-3</span></td>
897− <td><span class="time">9.71574644699696e-3</span></td>
898− <td><span class="confinterval citime">1.3200731985635176e-2</span></td>
899− </tr>
900− </tbody>
901− </table>
902−
903− <span class="outliers">
904− <p>Outlying measurements have moderate
905− (<span class="percent">0.18749999999999997</span>%)
906− effect on estimated standard deviation.</p>
907− </span>
908−
909− <h2><a name="grokularation">understanding this report</a></h2>
910−
911− <p>In this report, each function benchmarked by criterion is assigned
912− a section of its own. The charts in each section are active; if
913− you hover your mouse over data points and annotations, you will see
914− more details.</p>
915−
916− <ul>
917− <li>The chart on the left is a
918− <a href="http://en.wikipedia.org/wiki/Kernel_density_estimation">kernel
919− density estimate</a> (also known as a KDE) of time
920− measurements. This graphs the probability of any given time
921− measurement occurring. A spike indicates that a measurement of a
922− particular time occurred; its height indicates how often that
923− measurement was repeated.</li>
924−
925− <li>The chart on the right is the raw data from which the kernel
926− density estimate is built. The <i>x</i> axis indicates the
927− number of loop iterations, while the <i>y</i> axis shows measured
928− execution time for the given number of loop iterations. The
929− line behind the values is the linear regression prediction of
930− execution time for a given number of iterations. Ideally, all
931− measurements will be on (or very near) this line.</li>
932− </ul>
933−
934− <p>Under the charts is a small table.
935− The first two rows are the results of a linear regression run
936− on the measurements displayed in the right-hand chart.</p>
937−
938− <ul>
939− <li><i>OLS regression</i> indicates the
940− time estimated for a single loop iteration using an ordinary
941− least-squares regression model. This number is more accurate
942− than the <i>mean</i> estimate below it, as it more effectively
943− eliminates measurement overhead and other constant factors.</li>
944− <li><i>R&#xb2; goodness-of-fit</i> is a measure of how
945− accurately the linear regression model fits the observed
946− measurements. If the measurements are not too noisy, R&#xb2;
947− should lie between 0.99 and 1, indicating an excellent fit. If
948− the number is below 0.99, something is confounding the accuracy
949− of the linear model.</li>
950− <li><i>Mean execution time</i> and <i>standard deviation</i> are
951− statistics calculated from execution time
952− divided by number of iterations.</li>
953− </ul>
954−
955− <p>We use a statistical technique called
956− the <a href="http://en.wikipedia.org/wiki/Bootstrapping_(statistics)">bootstrap</a>
957− to provide confidence intervals on our estimates. The
958− bootstrap-derived upper and lower bounds on estimates let you see
959− how accurate we believe those estimates to be. (Hover the mouse
960− over the table headers to see the confidence levels.)</p>
961−
962− <p>A noisy benchmarking environment can cause some or many
963− measurements to fall far from the mean. These outlying
964− measurements can have a significant inflationary effect on the
965− estimate of the standard deviation. We calculate and display an
966− estimate of the extent to which the standard deviation has been
967− inflated by outliers.</p>
968−
969−<script type="text/javascript">
970−$(function () {
971− function mangulate(rpt) {
972− var measured = function(key) {
973− var idx = rpt.reportKeys.indexOf(key);
974− return rpt.reportMeasured.map(function(r) { return r[idx]; });
975− };
976− var lowerBound = function(est) {
977− return est.estPoint - est.estError.confIntLDX;
978− };
979− var upperBound = function(est) {
980− return est.estPoint + est.estError.confIntUDX;
981− };
982− var number = rpt.reportNumber;
983− var name = rpt.reportName;
984− var mean = rpt.reportAnalysis.anMean.estPoint;
985− var iters = measured("iters");
986− var times = measured("time");
987− var kdetimes = rpt.reportKDEs[0].kdeValues;
988− var kdepdf = rpt.reportKDEs[0].kdePDF;
989−
990− var meanSecs = mean;
991− var units = $.timeUnits(mean);
992− var rgrs = rpt.reportAnalysis.anRegress[0];
993− var scale = units[0];
994− var olsTime = rgrs.regCoeffs.iters;
995− $(".olstimept" + number).text(function() {
996− return $.renderTime(olsTime.estPoint);
997− });
998− $(".olstimelb" + number).text(function() {
999− return $.renderTime(lowerBound(olsTime));
1000− });
1001− $(".olstimeub" + number).text(function() {
1002− return $.renderTime(upperBound(olsTime));
1003− });
1004− $(".olsr2pt" + number).text(function() {
1005− return rgrs.regRSquare.estPoint.toFixed(3);
1006− });
1007− $(".olsr2lb" + number).text(function() {
1008− return lowerBound(rgrs.regRSquare).toFixed(3);
1009− });
1010− $(".olsr2ub" + number).text(function() {
1011− return upperBound(rgrs.regRSquare).toFixed(3);
1012− });
1013− mean *= scale;
1014− kdetimes = $.scaleBy(scale, kdetimes);
1015− var kq = $("#kde" + number);
1016− var k = $.plot(kq,
1017− [{ label: name + " time densities",
1018− data: $.zip(kdetimes, kdepdf),
1019− }],
1020− { xaxis: { tickFormatter: $.unitFormatter(scale) },
1021− yaxis: { ticks: false },
1022− grid: { borderColor: "#777",
1023− hoverable: true, markings: [ { color: '#6fd3fb',
1024− lineWidth: 1.5, xaxis: { from: mean, to: mean } } ] },
1025− });
1026− var o = k.pointOffset({ x: mean, y: 0});
1027− kq.append('<div class="meanlegend" title="' + $.renderTime(meanSecs) +
1028− '" style="position:absolute;left:' + (o.left + 4) +
1029− 'px;bottom:139px;">mean</div>');
1030− $.addTooltip("#kde" + number,
1031− function(secs) { return $.renderTime(secs / scale); });
1032− var timepairs = new Array(times.length);
1033− var lastiter = iters[iters.length-1];
1034− var olspairs = [[0,0], [lastiter, lastiter * scale * olsTime.estPoint]];
1035− for (var i = 0; i < times.length; i++)
1036− timepairs[i] = [iters[i],times[i]*scale];
1037− iterFormatter = function() {
1038− var denom = 0;
1039− return function(iters) {
1040− if (iters == 0)
1041− return '';
1042− if (denom > 0)
1043− return (iters / denom).toFixed();
1044− var power;
1045− if (iters >= 1e9) {
1046− denom = '1e9'; power = '&#x2079;';
1047− }
1048− if (iters >= 1e6) {
1049− denom = '1e6'; power = '&#x2076;';
1050− }
1051− else if (iters >= 1e3) {
1052− denom = '1e3'; power = '&#xb3;';
1053− }
1054− else denom = 1;
1055− if (denom > 1) {
1056− iters = (iters / denom).toFixed();
1057− iters += '&times;10' + power + ' iters';
1058− } else {
1059− iters += ' iters';
1060− }
1061− return iters;
1062− };
1063− };
1064− $.plot($("#time" + number),
1065− [{ label: "regression", data: olspairs,
1066− lines: { show: true } },
1067− { label: name + " times", data: timepairs,
1068− points: { show: true } }],
1069− { grid: { borderColor: "#777", hoverable: true },
1070− xaxis: { tickFormatter: iterFormatter() },
1071− yaxis: { tickFormatter: $.unitFormatter(scale) } });
1072− $.addTooltip("#time" + number,
1073− function(iters,secs) {
1074− return ($.renderTime(secs / scale) + ' / ' +
1075− iters.toLocaleString() + ' iters');
1076− });
1077− if (0) {
1078− var cyclepairs = new Array(cycles.length);
1079− for (var i = 0; i < cycles.length; i++)
1080− cyclepairs[i] = [cycles[i],i];
1081− $.plot($("#cycle" + number),
1082− [{ label: name + " cycles",
1083− data: cyclepairs }],
1084− { points: { show: true },
1085− grid: { borderColor: "#777", hoverable: true },
1086− xaxis: { tickFormatter:
1087− function(cycles,axis) { return cycles + ' cycles'; }},
1088− yaxis: { ticks: false },
1089− });
1090− $.addTooltip("#cycles" + number, function(x,y) { return x + ' cycles'; });
1091− }
1092− };
1093− var reports = 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vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Selective version","reportOutliers":{"highSevere":0,"highMild":0,"lowMild":0,"samplesSeen":4,"lowSevere":0},"reportMeasured":[[0.5517748849961208,0.8263997859999961,1320913854,1,null,null,null,null,null,null,null],[1.0859046559999115,1.626134200999985,2599589106,2,null,null,null,null,null,null,null],[1.609246867003094,2.446943054000002,3852436014,3,null,null,null,null,null,null,null],[2.1156421859996044,3.179338324999975,5064712443,4,null,null,null,null,null,null,null]]}];
1094− reports.map(mangulate);
1095−
1096− var benches = ["Matrix Composition/10x10/WHNF - 10x10","Matrix Composition/10x10/NF - 10x10","Matrix Composition/100x100/WHNF - 100x100","Matrix Composition/100x100/NF - 100x100","Matrix Composition/200x200/WHNF - 200x200","Matrix Composition/200x200/NF - 200x200","Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/List Distribution - Applicative version","Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Applicative version","Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Selective version","Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Applicative version","Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Selective version",];
1097− var ylabels = [[-0,"<a href=\"#b0\">Matrix Composition/10x10/WHNF - 10x10</a>"],[-1,"<a href=\"#b1\">Matrix Composition/10x10/NF - 10x10</a>"],[-2,"<a href=\"#b2\">Matrix Composition/100x100/WHNF - 100x100</a>"],[-3,"<a href=\"#b3\">Matrix Composition/100x100/NF - 100x100</a>"],[-4,"<a href=\"#b4\">Matrix Composition/200x200/WHNF - 200x200</a>"],[-5,"<a href=\"#b5\">Matrix Composition/200x200/NF - 200x200</a>"],[-6,"<a href=\"#b6\">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/List Distribution - Applicative version</a>"],[-7,"<a href=\"#b7\">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Applicative version</a>"],[-8,"<a href=\"#b8\">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Selective version</a>"],[-9,"<a href=\"#b9\">Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Applicative version</a>"],[-10,"<a href=\"#b10\">Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Selective version</a>"],];
1098− var means = $.scaleTimes([2.4514866756029205e-8,2.0678688880349948e-4,2.032500748180748e-8,0.3562973993334708,2.0667543937993357e-8,4.623436544688502,2.3105826970010335,2.672449312433179e-3,1.0459672120901292e-3,1.1506916786456713,0.5400133454575856,]);
1099− var stddevs = [9.647785488002723e-10,1.2679488723877765e-5,1.1748079988373767e-9,7.81042655007339e-3,1.8602778611455406e-9,6.412809734921064e-2,0.16563995052316,9.064184723100453e-4,2.169293235834326e-4,2.468474748870533e-2,9.71574644699696e-3,];
1100− stddevs = $.scaleBy(means[1], stddevs);
1101− var xs = [];
1102− var prev = null;
1103− var colors = ["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"];
1104− var max_time = 0;
1105− for (var i = 0; i < means[0].length; i++) {
1106− var name = benches[i].split(/\//);
1107− name.pop();
1108− name = name.join('/');
1109− if (name != prev) {
1110− var color = colors[xs.length / 2 % colors.length];
1111− xs.push({ data: [[means[0][i], -i]], label: name, color: color,
1112− bars: { show: true, horizontal: true, barWidth: 0.75,
1113− align: "center" } });
1114− xs.push({ data: [[means[0][i], -i, stddevs[i]]], color: color,
1115− lines: { show: false },
1116− points: { radius: 0, errorbars: "x",
1117− xerr: { show: true, radius: 5,
1118− lowerCap: "-", upperCap: "-" } } });
1119− prev = name;
1120− } else {
1121− xs[xs.length - 2].data.push([means[0][i], -i]);
1122− xs[xs.length - 1].data.push([means[0][i], -i, stddevs[i]]);
1123− }
1124− max_time = Math.max(max_time, means[0][i] + stddevs[i]);
1125− }
1126− var oq = $("#overview");
1127− var max_x = max_time * 1.02;
1128− o = $.plot(oq, xs, { grid: { borderColor: "#777", hoverable: true },
1129− legend: { show: xs.length > 1 },
1130− xaxis: { max: max_x, zoomRange: [false, max_x], panRange: [0, max_x] },
1131− yaxis: { ticks: ylabels, tickColor: '#ffffff', zoomRange: false, panRange: false },
1132− zoom: { interactive: true, amount: 1.05 },
1133− pan: { interactive: true } });
1134− if (benches.length > 3)
1135− o.getPlaceholder().height(28*benches.length);
1136− o.resize();
1137− o.setupGrid();
1138− o.draw();
1139− $.addTooltip("#overview", function(x,y) { return $.renderTime(x / means[1]); });
1140−});
1141−$(document).ready(function () {
1142− $(".time").text(function(_, text) {
1143− return $.renderTime(text);
1144− });
1145− $(".citime").text(function(_, text) {
1146− return $.renderTime(text);
1147− });
1148− $(".percent").text(function(_, text) {
1149− return (text*100).toFixed(1);
1150− });
1151− });
1152−</script>
1153−
1154− </div>
1155− </div>
1156− <div id="footer">
1157− <div class="body">
1158− <div class="footfirst">
1159− <h2>colophon</h2>
1160− <p>This report was created using the
1161− <a href="http://hackage.haskell.org/package/criterion">criterion</a>
1162− benchmark execution and performance analysis tool.</p>
1163− <p>Criterion is developed and maintained
1164− by <a href="http://www.serpentine.com/blog/">Bryan O'Sullivan</a>.</p>
1165− </div>
1166− </div>
1167− </div>
1168− </body>
1169−</html>