on master help sign in
Added lazy and strict reports
commit6923995bdf29635593d50fe0703693ed542bfa30 authorArmando Santos <armandoifsantos@gmail.com> dateSat 8 Feb 2020, 19:15 (7 years ago) parenta78e6db
2 files changed+1258−89
report-lazy.html+1169 report.html → report-strict.html+89−89
report-lazy.htmlnew file+1169
@@ -0,0 +1,1169 @@
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2+<html>
3+ <head>
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5+ <title>criterion report</title>
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placeholder};plot.getCanvas=function(){return surface.element};plot.getPlotOffset=function(){return plotOffset};plot.width=function(){return plotWidth};plot.height=function(){return plotHeight};plot.offset=function(){var o=eventHolder.offset();o.left+=plotOffset.left;o.top+=plotOffset.top;return o};plot.getData=function(){return series};plot.getAxes=function(){var res={},i;$.each(xaxes.concat(yaxes),function(_,axis){if(axis)res[axis.direction+(axis.n!=1?axis.n:"")+"axis"]=axis});return res};plot.getXAxes=function(){return xaxes};plot.getYAxes=function(){return yaxes};plot.c2p=canvasToAxisCoords;plot.p2c=axisToCanvasCoords;plot.getOptions=function(){return options};plot.highlight=highlight;plot.unhighlight=unhighlight;plot.triggerRedrawOverlay=triggerRedrawOverlay;plot.pointOffset=function(point){return{left:parseInt(xaxes[axisNumber(point,"x")-1].p2c(+point.x)+plotOffset.left,10),top:parseInt(yaxes[axisNumber(point,"y")-1].p2c(+point.y)+plotOffset.top,10)}};plot.shutdown=shutdown;plot.destroy=function(){shutdown();placeholder.removeData("plot").empty();series=[];options=null;surface=null;overlay=null;eventHolder=null;ctx=null;octx=null;xaxes=[];yaxes=[];hooks=null;highlights=[];plot=null};plot.resize=function(){var width=placeholder.width(),height=placeholder.height();surface.resize(width,height);overlay.resize(width,height)};plot.hooks=hooks;initPlugins(plot);parseOptions(options_);setupCanvases();setData(data_);setupGrid();draw();bindEvents();function executeHooks(hook,args){args=[plot].concat(args);for(var i=0;i<hook.length;++i)hook[i].apply(this,args)}function initPlugins(){var classes={Canvas:Canvas};for(var i=0;i<plugins.length;++i){var p=plugins[i];p.init(plot,classes);if(p.options)$.extend(true,options,p.options)}}function parseOptions(opts){$.extend(true,options,opts);if(opts&&opts.colors){options.colors=opts.colors}if(options.xaxis.color==null)options.xaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.yaxis.color==null)options.yaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.xaxis.tickColor==null)options.xaxis.tickColor=options.grid.tickColor||options.xaxis.color;if(options.yaxis.tickColor==null)options.yaxis.tickColor=options.grid.tickColor||options.yaxis.color;if(options.grid.borderColor==null)options.grid.borderColor=options.grid.color;if(options.grid.tickColor==null)options.grid.tickColor=$.color.parse(options.grid.color).scale("a",.22).toString();var i,axisOptions,axisCount,fontSize=placeholder.css("font-size"),fontSizeDefault=fontSize?+fontSize.replace("px",""):13,fontDefaults={style:placeholder.css("font-style"),size:Math.round(.8*fontSizeDefault),variant:placeholder.css("font-variant"),weight:placeholder.css("font-weight"),family:placeholder.css("font-family")};axisCount=options.xaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.xaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.xaxis,axisOptions);options.xaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}axisCount=options.yaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.yaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.yaxis,axisOptions);options.yaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}if(options.xaxis.noTicks&&options.xaxis.ticks==null)options.xaxis.ticks=options.xaxis.noTicks;if(options.yaxis.noTicks&&options.yaxis.ticks==null)options.yaxis.ticks=options.yaxis.noTicks;if(options.x2axis){options.xaxes[1]=$.extend(true,{},options.xaxis,options.x2axis);options.xaxes[1].position="top";if(options.x2axis.min==null){options.xaxes[1].min=null}if(options.x2axis.max==null){options.xaxes[1].max=null}}if(options.y2axis){options.yaxes[1]=$.extend(true,{},options.yaxis,options.y2axis);options.yaxes[1].position="right";if(options.y2axis.min==null){options.yaxes[1].min=null}if(options.y2axis.max==null){options.yaxes[1].max=null}}if(options.grid.coloredAreas)options.grid.markings=options.grid.coloredAreas;if(options.grid.coloredAreasColor)options.grid.markingsColor=options.grid.coloredAreasColor;if(options.lines)$.extend(true,options.series.lines,options.lines);if(options.points)$.extend(true,options.series.points,options.points);if(options.bars)$.extend(true,options.series.bars,options.bars);if(options.shadowSize!=null)options.series.shadowSize=options.shadowSize;if(options.highlightColor!=null)options.series.highlightColor=options.highlightColor;for(i=0;i<options.xaxes.length;++i)getOrCreateAxis(xaxes,i+1).options=options.xaxes[i];for(i=0;i<options.yaxes.length;++i)getOrCreateAxis(yaxes,i+1).options=options.yaxes[i];for(var n in hooks)if(options.hooks[n]&&options.hooks[n].length)hooks[n]=hooks[n].concat(options.hooks[n]);executeHooks(hooks.processOptions,[options])}function setData(d){series=parseData(d);fillInSeriesOptions();processData()}function parseData(d){var res=[];for(var i=0;i<d.length;++i){var s=$.extend(true,{},options.series);if(d[i].data!=null){s.data=d[i].data;delete d[i].data;$.extend(true,s,d[i]);d[i].data=s.data}else s.data=d[i];res.push(s)}return res}function axisNumber(obj,coord){var a=obj[coord+"axis"];if(typeof a=="object")a=a.n;if(typeof a!="number")a=1;return a}function allAxes(){return $.grep(xaxes.concat(yaxes),function(a){return a})}function canvasToAxisCoords(pos){var res={},i,axis;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used)res["x"+axis.n]=axis.c2p(pos.left)}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used)res["y"+axis.n]=axis.c2p(pos.top)}if(res.x1!==undefined)res.x=res.x1;if(res.y1!==undefined)res.y=res.y1;return res}function axisToCanvasCoords(pos){var res={},i,axis,key;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used){key="x"+axis.n;if(pos[key]==null&&axis.n==1)key="x";if(pos[key]!=null){res.left=axis.p2c(pos[key]);break}}}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used){key="y"+axis.n;if(pos[key]==null&&axis.n==1)key="y";if(pos[key]!=null){res.top=axis.p2c(pos[key]);break}}}return res}function getOrCreateAxis(axes,number){if(!axes[number-1])axes[number-1]={n:number,direction:axes==xaxes?"x":"y",options:$.extend(true,{},axes==xaxes?options.xaxis:options.yaxis)};return axes[number-1]}function fillInSeriesOptions(){var neededColors=series.length,maxIndex=-1,i;for(i=0;i<series.length;++i){var sc=series[i].color;if(sc!=null){neededColors--;if(typeof sc=="number"&&sc>maxIndex){maxIndex=sc}}}if(neededColors<=maxIndex){neededColors=maxIndex+1}var c,colors=[],colorPool=options.colors,colorPoolSize=colorPool.length,variation=0;for(i=0;i<neededColors;i++){c=$.color.parse(colorPool[i%colorPoolSize]||"#666");if(i%colorPoolSize==0&&i){if(variation>=0){if(variation<.5){variation=-variation-.2}else variation=0}else variation=-variation}colors[i]=c.scale("rgb",1+variation)}var colori=0,s;for(i=0;i<series.length;++i){s=series[i];if(s.color==null){s.color=colors[colori].toString();++colori}else if(typeof s.color=="number")s.color=colors[s.color].toString();if(s.lines.show==null){var v,show=true;for(v in s)if(s[v]&&s[v].show){show=false;break}if(show)s.lines.show=true}if(s.lines.zero==null){s.lines.zero=!!s.lines.fill}s.xaxis=getOrCreateAxis(xaxes,axisNumber(s,"x"));s.yaxis=getOrCreateAxis(yaxes,axisNumber(s,"y"))}}function processData(){var topSentry=Number.POSITIVE_INFINITY,bottomSentry=Number.NEGATIVE_INFINITY,fakeInfinity=Number.MAX_VALUE,i,j,k,m,length,s,points,ps,x,y,axis,val,f,p,data,format;function 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format[format.length-1].y;format[format.length-1].x=true}}s.datapoints.format=format}if(s.datapoints.pointsize!=null)continue;s.datapoints.pointsize=format.length;ps=s.datapoints.pointsize;points=s.datapoints.points;var insertSteps=s.lines.show&&s.lines.steps;s.xaxis.used=s.yaxis.used=true;for(j=k=0;j<data.length;++j,k+=ps){p=data[j];var nullify=p==null;if(!nullify){for(m=0;m<ps;++m){val=p[m];f=format[m];if(f){if(f.number&&val!=null){val=+val;if(isNaN(val))val=null;else if(val==Infinity)val=fakeInfinity;else if(val==-Infinity)val=-fakeInfinity}if(val==null){if(f.required)nullify=true;if(f.defaultValue!=null)val=f.defaultValue}}points[k+m]=val}}if(nullify){for(m=0;m<ps;++m){val=points[k+m];if(val!=null){f=format[m];if(f.autoscale!==false){if(f.x){updateAxis(s.xaxis,val,val)}if(f.y){updateAxis(s.yaxis,val,val)}}}points[k+m]=null}}else{if(insertSteps&&k>0&&points[k-ps]!=null&&points[k-ps]!=points[k]&&points[k-ps+1]!=points[k+1]){for(m=0;m<ps;++m)points[k+ps+m]=points[k+m];points[k+1]=points[k-ps+1];k+=ps}}}}for(i=0;i<series.length;++i){s=series[i];executeHooks(hooks.processDatapoints,[s,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];points=s.datapoints.points;ps=s.datapoints.pointsize;format=s.datapoints.format;var xmin=topSentry,ymin=topSentry,xmax=bottomSentry,ymax=bottomSentry;for(j=0;j<points.length;j+=ps){if(points[j]==null)continue;for(m=0;m<ps;++m){val=points[j+m];f=format[m];if(!f||f.autoscale===false||val==fakeInfinity||val==-fakeInfinity)continue;if(f.x){if(val<xmin)xmin=val;if(val>xmax)xmax=val}if(f.y){if(val<ymin)ymin=val;if(val>ymax)ymax=val}}}if(s.bars.show){var delta;switch(s.bars.align){case"left":delta=0;break;case"right":delta=-s.bars.barWidth;break;default:delta=-s.bars.barWidth/2}if(s.bars.horizontal){ymin+=delta;ymax+=delta+s.bars.barWidth}else{xmin+=delta;xmax+=delta+s.bars.barWidth}}updateAxis(s.xaxis,xmin,xmax);updateAxis(s.yaxis,ymin,ymax)}$.each(allAxes(),function(_,axis){if(axis.datamin==topSentry)axis.datamin=null;if(axis.datamax==bottomSentry)axis.datamax=null})}function 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setTransformationHelpers(axis){function identity(x){return x}var s,m,t=axis.options.transform||identity,it=axis.options.inverseTransform;if(axis.direction=="x"){s=axis.scale=plotWidth/Math.abs(t(axis.max)-t(axis.min));m=Math.min(t(axis.max),t(axis.min))}else{s=axis.scale=plotHeight/Math.abs(t(axis.max)-t(axis.min));s=-s;m=Math.max(t(axis.max),t(axis.min))}if(t==identity)axis.p2c=function(p){return(p-m)*s};else axis.p2c=function(p){return(t(p)-m)*s};if(!it)axis.c2p=function(c){return m+c/s};else axis.c2p=function(c){return it(m+c/s)}}function measureTickLabels(axis){var opts=axis.options,ticks=axis.ticks||[],labelWidth=opts.labelWidth||0,labelHeight=opts.labelHeight||0,maxWidth=labelWidth||(axis.direction=="x"?Math.floor(surface.width/(ticks.length||1)):null),legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=opts.font||"flot-tick-label tickLabel";for(var i=0;i<ticks.length;++i){var t=ticks[i];if(!t.label)continue;var info=surface.getTextInfo(layer,t.label,font,null,maxWidth);labelWidth=Math.max(labelWidth,info.width);labelHeight=Math.max(labelHeight,info.height)}axis.labelWidth=opts.labelWidth||labelWidth;axis.labelHeight=opts.labelHeight||labelHeight}function allocateAxisBoxFirstPhase(axis){var lw=axis.labelWidth,lh=axis.labelHeight,pos=axis.options.position,isXAxis=axis.direction==="x",tickLength=axis.options.tickLength,axisMargin=options.grid.axisMargin,padding=options.grid.labelMargin,innermost=true,outermost=true,first=true,found=false;$.each(isXAxis?xaxes:yaxes,function(i,a){if(a&&(a.show||a.reserveSpace)){if(a===axis){found=true}else if(a.options.position===pos){if(found){outermost=false}else{innermost=false}}if(!found){first=false}}});if(outermost){axisMargin=0}if(tickLength==null){tickLength=first?"full":5}if(!isNaN(+tickLength))padding+=+tickLength;if(isXAxis){lh+=padding;if(pos=="bottom"){plotOffset.bottom+=lh+axisMargin;axis.box={top:surface.height-plotOffset.bottom,height:lh}}else{axis.box={top:plotOffset.top+axisMargin,height:lh};plotOffset.top+=lh+axisMargin}}else{lw+=padding;if(pos=="left"){axis.box={left:plotOffset.left+axisMargin,width:lw};plotOffset.left+=lw+axisMargin}else{plotOffset.right+=lw+axisMargin;axis.box={left:surface.width-plotOffset.right,width:lw}}}axis.position=pos;axis.tickLength=tickLength;axis.box.padding=padding;axis.innermost=innermost}function allocateAxisBoxSecondPhase(axis){if(axis.direction=="x"){axis.box.left=plotOffset.left-axis.labelWidth/2;axis.box.width=surface.width-plotOffset.left-plotOffset.right+axis.labelWidth}else{axis.box.top=plotOffset.top-axis.labelHeight/2;axis.box.height=surface.height-plotOffset.bottom-plotOffset.top+axis.labelHeight}}function adjustLayoutForThingsStickingOut(){var minMargin=options.grid.minBorderMargin,axis,i;if(minMargin==null){minMargin=0;for(i=0;i<series.length;++i)minMargin=Math.max(minMargin,2*(series[i].points.radius+series[i].points.lineWidth/2))}var margins={left:minMargin,right:minMargin,top:minMargin,bottom:minMargin};$.each(allAxes(),function(_,axis){if(axis.reserveSpace&&axis.ticks&&axis.ticks.length){if(axis.direction==="x"){margins.left=Math.max(margins.left,axis.labelWidth/2);margins.right=Math.max(margins.right,axis.labelWidth/2)}else{margins.bottom=Math.max(margins.bottom,axis.labelHeight/2);margins.top=Math.max(margins.top,axis.labelHeight/2)}}});plotOffset.left=Math.ceil(Math.max(margins.left,plotOffset.left));plotOffset.right=Math.ceil(Math.max(margins.right,plotOffset.right));plotOffset.top=Math.ceil(Math.max(margins.top,plotOffset.top));plotOffset.bottom=Math.ceil(Math.max(margins.bottom,plotOffset.bottom))}function setupGrid(){var i,axes=allAxes(),showGrid=options.grid.show;for(var a in plotOffset){var margin=options.grid.margin||0;plotOffset[a]=typeof margin=="number"?margin:margin[a]||0}executeHooks(hooks.processOffset,[plotOffset]);for(var a in plotOffset){if(typeof options.grid.borderWidth=="object"){plotOffset[a]+=showGrid?options.grid.borderWidth[a]:0}else{plotOffset[a]+=showGrid?options.grid.borderWidth:0}}$.each(axes,function(_,axis){var axisOpts=axis.options;axis.show=axisOpts.show==null?axis.used:axisOpts.show;axis.reserveSpace=axisOpts.reserveSpace==null?axis.show:axisOpts.reserveSpace;setRange(axis)});if(showGrid){var allocatedAxes=$.grep(axes,function(axis){return axis.show||axis.reserveSpace});$.each(allocatedAxes,function(_,axis){setupTickGeneration(axis);setTicks(axis);snapRangeToTicks(axis,axis.ticks);measureTickLabels(axis)});for(i=allocatedAxes.length-1;i>=0;--i)allocateAxisBoxFirstPhase(allocatedAxes[i]);adjustLayoutForThingsStickingOut();$.each(allocatedAxes,function(_,axis){allocateAxisBoxSecondPhase(axis)})}plotWidth=surface.width-plotOffset.left-plotOffset.right;plotHeight=surface.height-plotOffset.bottom-plotOffset.top;$.each(axes,function(_,axis){setTransformationHelpers(axis)});if(showGrid){drawAxisLabels()}insertLegend()}function setRange(axis){var opts=axis.options,min=+(opts.min!=null?opts.min:axis.datamin),max=+(opts.max!=null?opts.max:axis.datamax),delta=max-min;if(delta==0){var widen=max==0?1:.01;if(opts.min==null)min-=widen;if(opts.max==null||opts.min!=null)max+=widen}else{var margin=opts.autoscaleMargin;if(margin!=null){if(opts.min==null){min-=delta*margin;if(min<0&&axis.datamin!=null&&axis.datamin>=0)min=0}if(opts.max==null){max+=delta*margin;if(max>0&&axis.datamax!=null&&axis.datamax<=0)max=0}}}axis.min=min;axis.max=max}function setupTickGeneration(axis){var opts=axis.options;var noTicks;if(typeof opts.ticks=="number"&&opts.ticks>0)noTicks=opts.ticks;else noTicks=.3*Math.sqrt(axis.direction=="x"?surface.width:surface.height);var delta=(axis.max-axis.min)/noTicks,dec=-Math.floor(Math.log(delta)/Math.LN10),maxDec=opts.tickDecimals;if(maxDec!=null&&dec>maxDec){dec=maxDec}var magn=Math.pow(10,-dec),norm=delta/magn,size;if(norm<1.5){size=1}else if(norm<3){size=2;if(norm>2.25&&(maxDec==null||dec+1<=maxDec)){size=2.5;++dec}}else if(norm<7.5){size=5}else{size=10}size*=magn;if(opts.minTickSize!=null&&size<opts.minTickSize){size=opts.minTickSize}axis.delta=delta;axis.tickDecimals=Math.max(0,maxDec!=null?maxDec:dec);axis.tickSize=opts.tickSize||size;if(opts.mode=="time"&&!axis.tickGenerator){throw new Error("Time mode requires the flot.time plugin.")}if(!axis.tickGenerator){axis.tickGenerator=function(axis){var ticks=[],start=floorInBase(axis.min,axis.tickSize),i=0,v=Number.NaN,prev;do{prev=v;v=start+i*axis.tickSize;ticks.push(v);++i}while(v<axis.max&&v!=prev);return ticks};axis.tickFormatter=function(value,axis){var factor=axis.tickDecimals?Math.pow(10,axis.tickDecimals):1;var formatted=""+Math.round(value*factor)/factor;if(axis.tickDecimals!=null){var decimal=formatted.indexOf(".");var precision=decimal==-1?0:formatted.length-decimal-1;if(precision<axis.tickDecimals){return(precision?formatted:formatted+".")+(""+factor).substr(1,axis.tickDecimals-precision)}}return formatted}}if($.isFunction(opts.tickFormatter))axis.tickFormatter=function(v,axis){return""+opts.tickFormatter(v,axis)};if(opts.alignTicksWithAxis!=null){var otherAxis=(axis.direction=="x"?xaxes:yaxes)[opts.alignTicksWithAxis-1];if(otherAxis&&otherAxis.used&&otherAxis!=axis){var niceTicks=axis.tickGenerator(axis);if(niceTicks.length>0){if(opts.min==null)axis.min=Math.min(axis.min,niceTicks[0]);if(opts.max==null&&niceTicks.length>1)axis.max=Math.max(axis.max,niceTicks[niceTicks.length-1])}axis.tickGenerator=function(axis){var ticks=[],v,i;for(i=0;i<otherAxis.ticks.length;++i){v=(otherAxis.ticks[i].v-otherAxis.min)/(otherAxis.max-otherAxis.min);v=axis.min+v*(axis.max-axis.min);ticks.push(v)}return ticks};if(!axis.mode&&opts.tickDecimals==null){var extraDec=Math.max(0,-Math.floor(Math.log(axis.delta)/Math.LN10)+1),ts=axis.tickGenerator(axis);if(!(ts.length>1&&/\..*0$/.test((ts[1]-ts[0]).toFixed(extraDec))))axis.tickDecimals=extraDec}}}}function setTicks(axis){var oticks=axis.options.ticks,ticks=[];if(oticks==null||typeof oticks=="number"&&oticks>0)ticks=axis.tickGenerator(axis);else if(oticks){if($.isFunction(oticks))ticks=oticks(axis);else ticks=oticks}var i,v;axis.ticks=[];for(i=0;i<ticks.length;++i){var label=null;var t=ticks[i];if(typeof t=="object"){v=+t[0];if(t.length>1)label=t[1]}else v=+t;if(label==null)label=axis.tickFormatter(v,axis);if(!isNaN(v))axis.ticks.push({v:v,label:label})}}function snapRangeToTicks(axis,ticks){if(axis.options.autoscaleMargin&&ticks.length>0){if(axis.options.min==null)axis.min=Math.min(axis.min,ticks[0].v);if(axis.options.max==null&&ticks.length>1)axis.max=Math.max(axis.max,ticks[ticks.length-1].v)}}function draw(){surface.clear();executeHooks(hooks.drawBackground,[ctx]);var grid=options.grid;if(grid.show&&grid.backgroundColor)drawBackground();if(grid.show&&!grid.aboveData){drawGrid()}for(var i=0;i<series.length;++i){executeHooks(hooks.drawSeries,[ctx,series[i]]);drawSeries(series[i])}executeHooks(hooks.draw,[ctx]);if(grid.show&&grid.aboveData){drawGrid()}surface.render();triggerRedrawOverlay()}function extractRange(ranges,coord){var axis,from,to,key,axes=allAxes();for(var i=0;i<axes.length;++i){axis=axes[i];if(axis.direction==coord){key=coord+axis.n+"axis";if(!ranges[key]&&axis.n==1)key=coord+"axis";if(ranges[key]){from=ranges[key].from;to=ranges[key].to;break}}}if(!ranges[key]){axis=coord=="x"?xaxes[0]:yaxes[0];from=ranges[coord+"1"];to=ranges[coord+"2"]}if(from!=null&&to!=null&&from>to){var tmp=from;from=to;to=tmp}return{from:from,to:to,axis:axis}}function drawBackground(){ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.fillStyle=getColorOrGradient(options.grid.backgroundColor,plotHeight,0,"rgba(255, 255, 255, 0)");ctx.fillRect(0,0,plotWidth,plotHeight);ctx.restore()}function drawGrid(){var i,axes,bw,bc;ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var markings=options.grid.markings;if(markings){if($.isFunction(markings)){axes=plot.getAxes();axes.xmin=axes.xaxis.min;axes.xmax=axes.xaxis.max;axes.ymin=axes.yaxis.min;axes.ymax=axes.yaxis.max;markings=markings(axes)}for(i=0;i<markings.length;++i){var m=markings[i],xrange=extractRange(m,"x"),yrange=extractRange(m,"y");if(xrange.from==null)xrange.from=xrange.axis.min;if(xrange.to==null)xrange.to=xrange.axis.max;
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 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t=options.interaction.redrawOverlayInterval;if(t==-1){drawOverlay();return}if(!redrawTimeout)redrawTimeout=setTimeout(drawOverlay,t)}function drawOverlay(){redrawTimeout=null;octx.save();overlay.clear();octx.translate(plotOffset.left,plotOffset.top);var i,hi;for(i=0;i<highlights.length;++i){hi=highlights[i];if(hi.series.bars.show)drawBarHighlight(hi.series,hi.point);else drawPointHighlight(hi.series,hi.point)}octx.restore();executeHooks(hooks.drawOverlay,[octx])}function highlight(s,point,auto){if(typeof s=="number")s=series[s];if(typeof point=="number"){var ps=s.datapoints.pointsize;point=s.datapoints.points.slice(ps*point,ps*(point+1))}var i=indexOfHighlight(s,point);if(i==-1){highlights.push({series:s,point:point,auto:auto});triggerRedrawOverlay()}else if(!auto)highlights[i].auto=false}function unhighlight(s,point){if(s==null&&point==null){highlights=[];triggerRedrawOverlay();return}if(typeof s=="number")s=series[s];if(typeof point=="number"){var ps=s.datapoints.pointsize;point=s.datapoints.points.slice(ps*point,ps*(point+1))}var i=indexOfHighlight(s,point);if(i!=-1){highlights.splice(i,1);triggerRedrawOverlay()}}function indexOfHighlight(s,p){for(var i=0;i<highlights.length;++i){var h=highlights[i];if(h.series==s&&h.point[0]==p[0]&&h.point[1]==p[1])return i}return-1}function drawPointHighlight(series,point){var x=point[0],y=point[1],axisx=series.xaxis,axisy=series.yaxis,highlightColor=typeof series.highlightColor==="string"?series.highlightColor:$.color.parse(series.color).scale("a",.5).toString();if(x<axisx.min||x>axisx.max||y<axisy.min||y>axisy.max)return;var pointRadius=series.points.radius+series.points.lineWidth/2;octx.lineWidth=pointRadius;octx.strokeStyle=highlightColor;var radius=1.5*pointRadius;x=axisx.p2c(x);y=axisy.p2c(y);octx.beginPath();if(series.points.symbol=="circle")octx.arc(x,y,radius,0,2*Math.PI,false);else series.points.symbol(octx,x,y,radius,false);octx.closePath();octx.stroke()}function drawBarHighlight(series,point){var highlightColor=typeof series.highlightColor==="string"?series.highlightColor:$.color.parse(series.color).scale("a",.5).toString(),fillStyle=highlightColor,barLeft;switch(series.bars.align){case"left":barLeft=0;break;case"right":barLeft=-series.bars.barWidth;break;default:barLeft=-series.bars.barWidth/2}octx.lineWidth=series.bars.lineWidth;octx.strokeStyle=highlightColor;drawBar(point[0],point[1],point[2]||0,barLeft,barLeft+series.bars.barWidth,function(){return fillStyle},series.xaxis,series.yaxis,octx,series.bars.horizontal,series.bars.lineWidth)}function getColorOrGradient(spec,bottom,top,defaultColor){if(typeof spec=="string")return spec;else{var gradient=ctx.createLinearGradient(0,top,0,bottom);for(var i=0,l=spec.colors.length;i<l;++i){var c=spec.colors[i];if(typeof c!="string"){var co=$.color.parse(defaultColor);if(c.brightness!=null)co=co.scale("rgb",c.brightness);if(c.opacity!=null)co.a*=c.opacity;c=co.toString()}gradient.addColorStop(i/(l-1),c)}return gradient}}}$.plot=function(placeholder,data,options){var plot=new Plot($(placeholder),data,options,$.plot.plugins);return plot};$.plot.version="0.8.3";$.plot.plugins=[];$.fn.plot=function(data,options){return this.each(function(){$.plot(this,data,options)})};function floorInBase(n,base){return base*Math.floor(n/base)}})(jQuery);
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 drawError(ctx,err,x,y,upper,lower,drawUpper,drawLower,radius,offset,minmax){y+=offset;upper+=offset;lower+=offset;if(err.err=="x"){if(upper>x+radius)drawPath(ctx,[[upper,y],[Math.max(x+radius,minmax[0]),y]]);else drawUpper=false;if(lower<x-radius)drawPath(ctx,[[Math.min(x-radius,minmax[1]),y],[lower,y]]);else drawLower=false}else{if(upper<y-radius)drawPath(ctx,[[x,upper],[x,Math.min(y-radius,minmax[0])]]);else drawUpper=false;if(lower>y+radius)drawPath(ctx,[[x,Math.max(y+radius,minmax[1])],[x,lower]]);else drawLower=false}radius=err.radius!=null?err.radius:radius;if(drawUpper){if(err.upperCap=="-"){if(err.err=="x")drawPath(ctx,[[upper,y-radius],[upper,y+radius]]);else drawPath(ctx,[[x-radius,upper],[x+radius,upper]])}else if($.isFunction(err.upperCap)){if(err.err=="x")err.upperCap(ctx,upper,y,radius);else err.upperCap(ctx,x,upper,radius)}}if(drawLower){if(err.lowerCap=="-"){if(err.err=="x")drawPath(ctx,[[lower,y-radius],[lower,y+radius]]);else 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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257+ <![endif]-->
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+-->
398+ </tr>
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+-->
514+ </tr>
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+<!--
569+ <td><div id="cycle5" class="cyclechart"
570+ style="width:300px;height:278px;"></div></td>
571+-->
572+ </tr>
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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version","reportOutliers":{"highSevere":0,"highMild":0,"lowMild":1,"samplesSeen":4,"lowSevere":0},"reportMeasured":[[1.1142126609993284,1.7200968939999939,2667354309,1,null,null,null,null,null,null,null],[2.324254368999391,3.5069922490000067,5564118456,2,null,null,null,null,null,null,null],[3.4742067670013057,5.354769505999997,8317024569,3,null,null,null,null,null,null,null],[4.673431786999572,7.140673430000021,11187894510,4,null,null,null,null,null,null,null]]},{"reportAnalysis":{"anMean":{"estError":{"confIntLDX":9.226529999068589e-3,"confIntUDX":7.350261040452666e-3,"confIntCL":5.0e-2},"estPoint":0.5400133454575856},"anRegress":[{"regRSquare":{"estError":{"confIntLDX":1.9013483025909217e-5,"confIntUDX":1.427942716543118e-4,"confIntCL":5.0e-2},"estPoint":0.9998572057283457},"regResponder":"time","regCoeffs":{"y":{"estError":{"confIntLDX":1.926100600394486e-2,"confIntUDX":5.315479001727821e-2,"confIntCL":5.0e-2},"estPoint":3.690611999627592e-2},"iters":{"estError":{"confIntLDX":1.5099092404849968e-2,"confIntUDX":1.2635359602427343e-2,"confIntCL":5.0e-2},"estPoint":0.5214944114013628}}}],"anStdDev":{"estError":{"confIntLDX":5.382688893680855e-3,"confIntUDX":3.484985538638217e-3,"confIntCL":5.0e-2},"estPoint":9.71574644699696e-3},"anOutlierVar":{"ovFraction":0.18749999999999997,"ovDesc":"moderate","ovEffect":"Moderate"}},"reportKDEs":[{"kdeValues":[0.5266241126502791,0.5268401536439442,0.5270561946376092,0.5272722356312743,0.5274882766249394,0.5277043176186045,0.5279203586122695,0.5281363996059346,0.5283524405995996,0.5285684815932647,0.5287845225869298,0.5290005635805949,0.5292166045742599,0.529432645567925,0.52964868656159,0.5298647275552552,0.5300807685489202,0.5302968095425853,0.5305128505362503,0.5307288915299154,0.5309449325235804,0.5311609735172456,0.5313770145109106,0.5315930555045757,0.5318090964982407,0.5320251374919058,0.5322411784855708,0.532457219479236,0.532673260472901,0.5328893014665661,0.5331053424602311,0.5333213834538962,0.5335374244475612,0.5337534654412264,0.5339695064348914,0.5341855474285565,0.5344015884222215,0.5346176294158866,0.5348336704095517,0.5350497114032168,0.5352657523968818,0.5354817933905469,0.535697834384212,0.535913875377877,0.5361299163715422,0.5363459573652072,0.5365619983588723,0.5367780393525373,0.5369940803462024,0.5372101213398675,0.5374261623335326,0.5376422033271976,0.5378582443208627,0.5380742853145277,0.5382903263081928,0.5385063673018579,0.538722408295523,0.538938449289188,0.5391544902828531,0.5393705312765181,0.5395865722701833,0.5398026132638483,0.5400186542575134,0.5402346952511784,0.5404507362448435,0.5406667772385085,0.5408828182321737,0.5410988592258387,0.5413149002195038,0.5415309412131688,0.5417469822068339,0.5419630232004989,0.5421790641941641,0.5423951051878291,0.5426111461814942,0.5428271871751592,0.5430432281688243,0.5432592691624893,0.5434753101561545,0.5436913511498195,0.5439073921434846,0.5441234331371496,0.5443394741308147,0.5445555151244797,0.5447715561181449,0.5449875971118099,0.545203638105475,0.5454196790991401,0.5456357200928051,0.5458517610864703,0.5460678020801353,0.5462838430738004,0.5464998840674654,0.5467159250611305,0.5469319660547955,0.5471480070484607,0.5473640480421257,0.5475800890357908,0.5477961300294558,0.5480121710231209,0.548228212016786,0.5484442530104511,0.5486602940041161,0.5488763349977812,0.5490923759914462,0.5493084169851113,0.5495244579787764,0.5497404989724415,0.5499565399661065,0.5501725809597716,0.5503886219534366,0.5506046629471018,0.5508207039407668,0.5510367449344319,0.5512527859280969,0.551468826921762,0.551684867915427,0.5519009089090922,0.5521169499027572,0.5523329908964223,0.5525490318900873,0.5527650728837524,0.5529811138774174,0.5531971548710826,0.5534131958647476,0.5536292368584127,0.5538452778520777,0.5540613188457428],"kdeType":"time","kdePDF":[37.80415455765313,37.80292600654845,37.80047060039689,37.79679172815658,37.79189446537722,37.785785564752864,37.778473443554866,37.76996816797025,37.76028143437672,37.74942654759184,37.7374183961396,37.72427342458423,37.71000960298611,37.694646393541255,37.678204714471015,37.66070690123417,37.64217666513936,37.62263904944059,37.60212038300397,37.58064823163867,37.55825134718983,37.534959614495676,37.51080399631574,37.485816476340815,37.46003000039983,37.433478415982016,37.40619641019672,37.3782194462964,37.349583698891315,37.32032598798746,37.29048371198166,37.260094779750396,37.229197541970855,37.19783072181443,37.16603334515467,37.133844670433,37.101304118326205,37.06845120136111,37.035325453621546,37.001966360693594,36.96841328999454,36.934705421631,36.900881679930535,36.86698066579065,36.833040589987434,36.79909920758479,36.76519375358323,36.73136087994533,36.69763659413205,36.66405619928223,36.63065423616357,36.59746442702107,36.564519621444646,36.531851744374116,36.49949174635566,36.46746955615907,36.43581403586091,36.40455293849328,36.373712868353,36.34331924406034,36.313396264450546,36.28396687737609,36.25505275149016,36.22667425107673,36.19885041398497,36.17159893271934,36.14493613872926,36.118876989935536,36.09343506152256,36.06862254001848,36.044450220677085,36.02092750816791,35.998062420572595,35.9758615966779,35.95433030654729,35.933472465345126,35.913290650379025,35.89378612131797,35.87495884353536,35.85680751451812,35.83932959327476,35.82252133266752,35.80637781458526,35.79089298786665,35.77605970887489,35.76186978461828,35.748314018303226,35.735382257199674,35.723063442691675,35.7113456623798,35.700216204095526,35.689661611682034,35.679667742390244,35.67021982573381,35.66130252364202,35.65289999174517,35.644995941623144,35.637573703844325,35.630616291619255,35.62410646489062,35.61802679467938,35.612359727505336,35.607087649699444,35.602192951424875,35.59765809022356,35.59346565390639,35.58959842260569,35.586039429810846,35.5827720222102,35.579779918165265,35.57704726464653,35.574558692464635,35.57229936963464,35.570255052716576,35.56841213598098,35.5667576982539,35.56527954730286,35.563966261631876,35.56280722956131,35.561792685476064,35.560913743134016,35.56016242593501,35.55953169406004,35.55901546839895,35.55860865119522,35.55830714334559,35.558107858302684,35.55800873253867]}],"reportKeys":["time","cpuTime","cycles","iters","allocated","numGcs","bytesCopied","mutatorWallSeconds","mutatorCpuSeconds","gcWallSeconds","gcCpuSeconds"],"reportNumber":10,"reportName":"Matrix 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>
report.html → report-strict.htmlrenamed+89−89
@@ -307,22 +307,22 @@ h2 {
307307 </tr>
308308 <tr>
309309 <td>Mean execution time</td>
310− <td><span class="confinterval citime">1.2049433368049536e-4</span></td>
311− <td><span class="time">1.2273010647022493e-4</span></td>
312− <td><span class="confinterval citime">1.2778054268834733e-4</span></td>
310+ <td><span class="confinterval citime">1.2439476132780966e-4</span></td>
311+ <td><span class="time">1.2521282696584778e-4</span></td>
312+ <td><span class="confinterval citime">1.2627361635087712e-4</span></td>
313313 </tr>
314314 <tr>
315315 <td>Standard deviation</td>
316− <td><span class="confinterval citime">5.116451922891949e-6</span></td>
317− <td><span class="time">1.0291105469156734e-5</span></td>
318− <td><span class="confinterval citime">1.899929874113666e-5</span></td>
316+ <td><span class="confinterval citime">2.3769830641700065e-6</span></td>
317+ <td><span class="time">3.0000227210624054e-6</span></td>
318+ <td><span class="confinterval citime">3.868635052370606e-6</span></td>
319319 </tr>
320320 </tbody>
321321 </table>
322322
323323 <span class="outliers">
324− <p>Outlying measurements have severe
325− (<span class="percent">0.749671001723266</span>%)
324+ <p>Outlying measurements have moderate
325+ (<span class="percent">0.19510840974237945</span>%)
326326 effect on estimated standard deviation.</p>
327327 </span>
328328<h2><a name="b1">Matrix Composition/10x10/NF - 10x10</a></h2>
@@ -365,22 +365,22 @@ h2 {
365365 </tr>
366366 <tr>
367367 <td>Mean execution time</td>
368− <td><span class="confinterval citime">1.219807334985906e-4</span></td>
369− <td><span class="time">1.2343493665093922e-4</span></td>
370− <td><span class="confinterval citime">1.2633720476730494e-4</span></td>
368+ <td><span class="confinterval citime">1.4537321208713184e-4</span></td>
369+ <td><span class="time">1.532024430909428e-4</span></td>
370+ <td><span class="confinterval citime">1.6749129455977834e-4</span></td>
371371 </tr>
372372 <tr>
373373 <td>Standard deviation</td>
374− <td><span class="confinterval citime">4.876323665872892e-6</span></td>
375− <td><span class="time">7.497583796499309e-6</span></td>
376− <td><span class="confinterval citime">1.2305448428855409e-5</span></td>
374+ <td><span class="confinterval citime">2.365045193527146e-5</span></td>
375+ <td><span class="time">3.649213223188502e-5</span></td>
376+ <td><span class="confinterval citime">5.3596936354843454e-5</span></td>
377377 </tr>
378378 </tbody>
379379 </table>
380380
381381 <span class="outliers">
382382 <p>Outlying measurements have severe
383− (<span class="percent">0.6135229039528359</span>%)
383+ (<span class="percent">0.9609582426012205</span>%)
384384 effect on estimated standard deviation.</p>
385385 </span>
386386<h2><a name="b2">Matrix Composition/100x100/WHNF - 100x100</a></h2>
@@ -423,22 +423,22 @@ h2 {
423423 </tr>
424424 <tr>
425425 <td>Mean execution time</td>
426− <td><span class="confinterval citime">0.10670925100021122</span></td>
427− <td><span class="time">0.11259088430757033</span></td>
428− <td><span class="confinterval citime">0.12810259287901513</span></td>
426+ <td><span class="confinterval citime">0.11287564945528825</span></td>
427+ <td><span class="time">0.11922032795600032</span></td>
428+ <td><span class="confinterval citime">0.12600585030653141</span></td>
429429 </tr>
430430 <tr>
431431 <td>Standard deviation</td>
432− <td><span class="confinterval citime">2.794985106828957e-3</span></td>
433− <td><span class="time">1.4513815882466386e-2</span></td>
434− <td><span class="confinterval citime">2.275626878296965e-2</span></td>
432+ <td><span class="confinterval citime">6.3874541360917384e-3</span></td>
433+ <td><span class="time">9.77563027930971e-3</span></td>
434+ <td><span class="confinterval citime">1.620035460618151e-2</span></td>
435435 </tr>
436436 </tbody>
437437 </table>
438438
439439 <span class="outliers">
440440 <p>Outlying measurements have moderate
441− (<span class="percent">0.3584714096581915</span>%)
441+ (<span class="percent">0.23184395941421526</span>%)
442442 effect on estimated standard deviation.</p>
443443 </span>
444444<h2><a name="b3">Matrix Composition/100x100/NF - 100x100</a></h2>
@@ -481,22 +481,22 @@ h2 {
481481 </tr>
482482 <tr>
483483 <td>Mean execution time</td>
484− <td><span class="confinterval citime">0.10572328594183394</span></td>
485− <td><span class="time">0.11065191628694417</span></td>
486− <td><span class="confinterval citime">0.1190700679846183</span></td>
484+ <td><span class="confinterval citime">0.1110982578725974</span></td>
485+ <td><span class="time">0.11758616896428131</span></td>
486+ <td><span class="confinterval citime">0.12249179825349378</span></td>
487487 </tr>
488488 <tr>
489489 <td>Standard deviation</td>
490− <td><span class="confinterval citime">1.5396112567768426e-3</span></td>
491− <td><span class="time">9.633081000847341e-3</span></td>
492− <td><span class="confinterval citime">1.3776160319998043e-2</span></td>
490+ <td><span class="confinterval citime">5.369746750528417e-3</span></td>
491+ <td><span class="time">8.150134548496543e-3</span></td>
492+ <td><span class="confinterval citime">1.1065658668699107e-2</span></td>
493493 </tr>
494494 </tbody>
495495 </table>
496496
497497 <span class="outliers">
498498 <p>Outlying measurements have moderate
499− (<span class="percent">0.2338936359506027</span>%)
499+ (<span class="percent">0.22459068358328985</span>%)
500500 effect on estimated standard deviation.</p>
501501 </span>
502502<h2><a name="b4">Matrix Composition/200x200/WHNF - 200x200</a></h2>
@@ -539,15 +539,15 @@ h2 {
539539 </tr>
540540 <tr>
541541 <td>Mean execution time</td>
542− <td><span class="confinterval citime">0.9554592040840362</span></td>
543− <td><span class="time">0.9644028123334465</span></td>
544− <td><span class="confinterval citime">0.9742178227493241</span></td>
542+ <td><span class="confinterval citime">0.9781591890205164</span></td>
543+ <td><span class="time">1.0404128327718354</span></td>
544+ <td><span class="confinterval citime">1.0852378857525764</span></td>
545545 </tr>
546546 <tr>
547547 <td>Standard deviation</td>
548− <td><span class="confinterval citime">4.795288999957847e-3</span></td>
549− <td><span class="time">1.0832350628490363e-2</span></td>
550− <td><span class="confinterval citime">1.511725573135899e-2</span></td>
548+ <td><span class="confinterval citime">2.2371087252395228e-2</span></td>
549+ <td><span class="time">6.182803786470918e-2</span></td>
550+ <td><span class="confinterval citime">8.480026920295321e-2</span></td>
551551 </tr>
552552 </tbody>
553553 </table>
@@ -597,15 +597,15 @@ h2 {
597597 </tr>
598598 <tr>
599599 <td>Mean execution time</td>
600− <td><span class="confinterval citime">0.9654473458749635</span></td>
601− <td><span class="time">0.980254961521041</span></td>
602− <td><span class="confinterval citime">0.9994299113750458</span></td>
600+ <td><span class="confinterval citime">1.1011682298167216</span></td>
601+ <td><span class="time">1.1075485033964771</span></td>
602+ <td><span class="confinterval citime">1.1151673705599023</span></td>
603603 </tr>
604604 <tr>
605605 <td>Standard deviation</td>
606− <td><span class="confinterval citime">2.345238647867079e-4</span></td>
607− <td><span class="time">2.1335106928918192e-2</span></td>
608− <td><span class="confinterval citime">2.623796574056436e-2</span></td>
606+ <td><span class="confinterval citime">2.193747621276998e-3</span></td>
607+ <td><span class="time">7.979551817654461e-3</span></td>
608+ <td><span class="confinterval citime">1.077654356866516e-2</span></td>
609609 </tr>
610610 </tbody>
611611 </table>
@@ -615,7 +615,7 @@ h2 {
615615 (<span class="percent">0.1875</span>%)
616616 effect on estimated standard deviation.</p>
617617 </span>
618−<h2><a name="b6">Matrix vs List - `select`/Distribution/List Distribution - Applicative version</a></h2>
618+<h2><a name="b6">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/List Distribution - Applicative version</a></h2>
619619 <table width="100%">
620620 <tbody>
621621 <tr>
@@ -655,25 +655,25 @@ h2 {
655655 </tr>
656656 <tr>
657657 <td>Mean execution time</td>
658− <td><span class="confinterval citime">0.43770282037348807</span></td>
659− <td><span class="time">0.4467186700617276</span></td>
660− <td><span class="confinterval citime">0.46234597762486374</span></td>
658+ <td><span class="confinterval citime">2.338956891665778</span></td>
659+ <td><span class="time">2.3478542657700623</span></td>
660+ <td><span class="confinterval citime">2.359088486582247</span></td>
661661 </tr>
662662 <tr>
663663 <td>Standard deviation</td>
664− <td><span class="confinterval citime">1.3631004574408759e-3</span></td>
665− <td><span class="time">1.5029085673470916e-2</span></td>
666− <td><span class="confinterval citime">1.8726410685291874e-2</span></td>
664+ <td><span class="confinterval citime">3.62336445641734e-3</span></td>
665+ <td><span class="time">1.2441745894453989e-2</span></td>
666+ <td><span class="confinterval citime">1.67050971444586e-2</span></td>
667667 </tr>
668668 </tbody>
669669 </table>
670670
671671 <span class="outliers">
672672 <p>Outlying measurements have moderate
673− (<span class="percent">0.1875</span>%)
673+ (<span class="percent">0.18749999999999997</span>%)
674674 effect on estimated standard deviation.</p>
675675 </span>
676−<h2><a name="b7">Matrix vs List - `select`/Distribution/Matrix Distribution - Applicative version</a></h2>
676+<h2><a name="b7">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Applicative version</a></h2>
677677 <table width="100%">
678678 <tbody>
679679 <tr>
@@ -713,25 +713,25 @@ h2 {
713713 </tr>
714714 <tr>
715715 <td>Mean execution time</td>
716− <td><span class="confinterval citime">0.176601423499556</span></td>
717− <td><span class="time">0.1820171075632162</span></td>
718− <td><span class="confinterval citime">0.18602406437478045</span></td>
716+ <td><span class="confinterval citime">1.6422040204675979e-3</span></td>
717+ <td><span class="time">1.7931655457283859e-3</span></td>
718+ <td><span class="confinterval citime">1.9714955275173672e-3</span></td>
719719 </tr>
720720 <tr>
721721 <td>Standard deviation</td>
722− <td><span class="confinterval citime">4.2476576439371165e-3</span></td>
723− <td><span class="time">6.298418696687028e-3</span></td>
724− <td><span class="confinterval citime">8.492295568985392e-3</span></td>
722+ <td><span class="confinterval citime">3.9388340187426643e-4</span></td>
723+ <td><span class="time">5.021999918501304e-4</span></td>
724+ <td><span class="confinterval citime">6.587054587998915e-4</span></td>
725725 </tr>
726726 </tbody>
727727 </table>
728728
729729 <span class="outliers">
730− <p>Outlying measurements have moderate
731− (<span class="percent">0.1388888888888888</span>%)
730+ <p>Outlying measurements have severe
731+ (<span class="percent">0.9470035753933379</span>%)
732732 effect on estimated standard deviation.</p>
733733 </span>
734−<h2><a name="b8">Matrix vs List - `select`/Distribution/Matrix Distribution - Selective version</a></h2>
734+<h2><a name="b8">Matrix vs List - `select`/Distribution `select` - 100+100 / 100x100/Matrix Distribution - Selective version</a></h2>
735735 <table width="100%">
736736 <tbody>
737737 <tr>
@@ -771,25 +771,25 @@ h2 {
771771 </tr>
772772 <tr>
773773 <td>Mean execution time</td>
774− <td><span class="confinterval citime">8.933987364670819e-2</span></td>
775− <td><span class="time">9.136040249002575e-2</span></td>
776− <td><span class="confinterval citime">9.678633084819704e-2</span></td>
774+ <td><span class="confinterval citime">8.252875148785076e-4</span></td>
775+ <td><span class="time">9.038536710022285e-4</span></td>
776+ <td><span class="confinterval citime">1.0137042058789136e-3</span></td>
777777 </tr>
778778 <tr>
779779 <td>Standard deviation</td>
780− <td><span class="confinterval citime">1.4344054494409147e-3</span></td>
781− <td><span class="time">5.3416383323926005e-3</span></td>
782− <td><span class="confinterval citime">9.301990711614968e-3</span></td>
780+ <td><span class="confinterval citime">2.3352589892872938e-4</span></td>
781+ <td><span class="time">3.1773226135504875e-4</span></td>
782+ <td><span class="confinterval citime">4.273324974311236e-4</span></td>
783783 </tr>
784784 </tbody>
785785 </table>
786786
787787 <span class="outliers">
788− <p>Outlying measurements have moderate
789− (<span class="percent">0.18171701654949143</span>%)
788+ <p>Outlying measurements have severe
789+ (<span class="percent">0.984823670419889</span>%)
790790 effect on estimated standard deviation.</p>
791791 </span>
792−<h2><a name="b9">Matrix vs List - `select`/Matrix `select`/Applicative version</a></h2>
792+<h2><a name="b9">Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Applicative version</a></h2>
793793 <table width="100%">
794794 <tbody>
795795 <tr>
@@ -829,25 +829,25 @@ h2 {
829829 </tr>
830830 <tr>
831831 <td>Mean execution time</td>
832− <td><span class="confinterval citime">0.7791034777492314</span></td>
833− <td><span class="time">0.840422457165308</span></td>
834− <td><span class="confinterval citime">0.8923891394988459</span></td>
832+ <td><span class="confinterval citime">0.20685697058300623</span></td>
833+ <td><span class="time">0.20792234497182815</span></td>
834+ <td><span class="confinterval citime">0.20898771936065005</span></td>
835835 </tr>
836836 <tr>
837837 <td>Standard deviation</td>
838− <td><span class="confinterval citime">2.769907342949611e-2</span></td>
839− <td><span class="time">6.898641320796234e-2</span></td>
840− <td><span class="confinterval citime">9.23128387071436e-2</span></td>
838+ <td><span class="confinterval citime">1.11631780898578e-3</span></td>
839+ <td><span class="time">1.5104002558460128e-3</span></td>
840+ <td><span class="confinterval citime">1.827809373068682e-3</span></td>
841841 </tr>
842842 </tbody>
843843 </table>
844844
845845 <span class="outliers">
846846 <p>Outlying measurements have moderate
847− (<span class="percent">0.21376667254855866</span>%)
847+ (<span class="percent">0.1388888888888886</span>%)
848848 effect on estimated standard deviation.</p>
849849 </span>
850−<h2><a name="b10">Matrix vs List - `select`/Matrix `select`/Selective version</a></h2>
850+<h2><a name="b10">Matrix vs List - `select`/Matrix `select` - 100x(100+100) / 100x100/Selective version</a></h2>
851851 <table width="100%">
852852 <tbody>
853853 <tr>
@@ -887,22 +887,22 @@ h2 {
887887 </tr>
888888 <tr>
889889 <td>Mean execution time</td>
890− <td><span class="confinterval citime">0.41360511700137675</span></td>
891− <td><span class="time">0.42580509562564356</span></td>
892− <td><span class="confinterval citime">0.43034074300067005</span></td>
890+ <td><span class="confinterval citime">0.1040023584088178</span></td>
891+ <td><span class="time">0.10482248682469514</span></td>
892+ <td><span class="confinterval citime">0.10534815795558282</span></td>
893893 </tr>
894894 <tr>
895895 <td>Standard deviation</td>
896− <td><span class="confinterval citime">1.588963214600777e-4</span></td>
897− <td><span class="time">8.497480607539406e-3</span></td>
898− <td><span class="confinterval citime">1.0474628931683076e-2</span></td>
896+ <td><span class="confinterval citime">5.86339084666882e-4</span></td>
897+ <td><span class="time">1.080285904740125e-3</span></td>
898+ <td><span class="confinterval citime">1.5634852865472032e-3</span></td>
899899 </tr>
900900 </tbody>
901901 </table>
902902
903903 <span class="outliers">
904− <p>Outlying measurements have moderate
905− (<span class="percent">0.1875</span>%)
904+ <p>Outlying measurements have slight
905+ (<span class="percent">9.87654320987654e-2</span>%)
906906 effect on estimated standard deviation.</p>
907907 </span>
908908
@@ -1090,13 +1090,13 @@ $(function () {
10901090 $.addTooltip("#cycles" + number, function(x,y) { return x + ' cycles'; });
10911091 }
10921092 };
1093− var reports = 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10941094 reports.map(mangulate);
10951095
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/List Distribution - Applicative version","Matrix vs List - `select`/Distribution/Matrix Distribution - Applicative version","Matrix vs List - `select`/Distribution/Matrix Distribution - Selective version","Matrix vs List - `select`/Matrix `select`/Applicative version","Matrix vs List - `select`/Matrix `select`/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/List Distribution - Applicative version</a>"],[-7,"<a href=\"#b7\">Matrix vs List - `select`/Distribution/Matrix Distribution - Applicative version</a>"],[-8,"<a href=\"#b8\">Matrix vs List - `select`/Distribution/Matrix Distribution - Selective version</a>"],[-9,"<a href=\"#b9\">Matrix vs List - `select`/Matrix `select`/Applicative version</a>"],[-10,"<a href=\"#b10\">Matrix vs List - `select`/Matrix `select`/Selective version</a>"],];
1098− var means = $.scaleTimes([1.2273010647022493e-4,1.2343493665093922e-4,0.11259088430757033,0.11065191628694417,0.9644028123334465,0.980254961521041,0.4467186700617276,0.1820171075632162,9.136040249002575e-2,0.840422457165308,0.42580509562564356,]);
1099− var stddevs = [1.0291105469156734e-5,7.497583796499309e-6,1.4513815882466386e-2,9.633081000847341e-3,1.0832350628490363e-2,2.1335106928918192e-2,1.5029085673470916e-2,6.298418696687028e-3,5.3416383323926005e-3,6.898641320796234e-2,8.497480607539406e-3,];
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([1.2521282696584778e-4,1.532024430909428e-4,0.11922032795600032,0.11758616896428131,1.0404128327718354,1.1075485033964771,2.3478542657700623,1.7931655457283859e-3,9.038536710022285e-4,0.20792234497182815,0.10482248682469514,]);
1099+ var stddevs = [3.0000227210624054e-6,3.649213223188502e-5,9.77563027930971e-3,8.150134548496543e-3,6.182803786470918e-2,7.979551817654461e-3,1.2441745894453989e-2,5.021999918501304e-4,3.1773226135504875e-4,1.5104002558460128e-3,1.080285904740125e-3,];
11001100 stddevs = $.scaleBy(means[1], stddevs);
11011101 var xs = [];
11021102 var prev = null;