# Models With Multiple Random-effects Terms

The mixed models considered in the previous chapter had only one random-effects term, which was a simple, scalar random-effects term, and a single fixed-effects coefficient. Although such models can be useful, it is with the facility to use multiple random-effects terms and to use random-effects terms beyond a simple, scalar term that we can begin to realize the flexibility and versatility of mixed models.

In this chapter we consider models with multiple simple, scalar random-effects terms, showing examples where the grouping factors for these terms are in completely crossed or nested or partially crossed configurations. For ease of description we will refer to the random effects as being crossed or nested although, strictly speaking, the distinction between nested and non-nested refers to the grouping factors, not the random effects.



In [1]:

using DataFrames, Distributions, Feather, FreqTables, Gadfly, MixedModels
const datadir = Pkg.dir("MixedModels", "test", "data");
const ppt250 = inv(500) : inv(250) : 1.;
const zquantiles = quantile(Normal(), ppt250);
function hpdinterval(v, level=0.95)
n = length(v)
if !(0 < level < 1)
throw(ArgumentError("level = $level must be in (0, 1)")) end if (lbd = floor(Int, (1 - level) * n)) < 2 throw(ArgumentError( "level =$level is too large from sample size $n")) end ordstat = sort(v) leftendpts = ordstat[1:lbd] rtendpts = ordstat[(1 + n - lbd):n] (w, ind) = findmin(rtendpts - leftendpts) return [leftendpts[ind], rtendpts[ind]] end   Out[1]: hpdinterval (generic function with 2 methods)  ## A Model With Crossed Random Effects One of the areas in which the methods in the package for are particularly effective is in fitting models to cross-classified data where several factors have random effects associated with them. For example, in many experiments in psychology the reaction of each of a group of subjects to each of a group of stimuli or items is measured. If the subjects are considered to be a sample from a population of subjects and the items are a sample from a population of items, then it would make sense to associate random effects with both these factors. In the past it was difficult to fit mixed models with multiple, crossed grouping factors to large, possibly unbalanced, data sets. The methods in the package are able to do this. To introduce the methods let us first consider a small, balanced data set with crossed grouping factors. ### The Penicillin Data The data are derived from Table 6.6, p. 144 of where they are described as coming from an investigation to assess the variability between samples of penicillin by the B. subtilis method. In this test method a bulk-innoculated nutrient agar medium is poured into a Petri dish of approximately 90 mm. diameter, known as a plate. When the medium has set, six small hollow cylinders or pots (about 4 mm. in diameter) are cemented onto the surface at equally spaced intervals. A few drops of the penicillin solutions to be compared are placed in the respective cylinders, and the whole plate is placed in an incubator for a given time. Penicillin diffuses from the pots into the agar, and this produces a clear circular zone of inhibition of growth of the organisms, which can be readily measured. The diameter of the zone is related in a known way to the concentration of penicillin in the solution. As with the dyestuff data, we load penicillin from a feather-format file  In [2]: penicillin = Feather.read(joinpath(datadir, "Penicillin.feather"), nullable = false); names(penicillin)   Out[2]: 3-element Array{Symbol,1}: :diameter :plate :sample  of the data, then plot it [fig:Penicillindot] (Fig. [fig:Penicillindot]). The variation in the diameter is associated with the plates and with the samples. Because each plate is used only for the six samples shown here we are not interested in the contributions of specific plates as much as we are interested in the variation due to plates and in assessing the potency of the samples after accounting for this variation. Thus, we will use random effects for the factor. We will also use random effects for the factor because, as in the dyestuff example, we are more interested in the sample-to-sample variability in the penicillin samples than in the potency of a particular sample. In this experiment each sample is used on each plate. We say that the and factors are crossed, as opposed to nested factors, which we will describe in the next section. By itself, the designation “crossed” just means that the factors are not nested. If we wish to be more specific, we could describe these factors as being completely crossed, which means that we have at least one observation for each combination of a level of and a level of . We can see this in Fig. [fig:Penicillindot] and, because there are moderate numbers of levels in these factors, we can check it in a cross-tabulation  In [3]: freqtable(convert(Vector{String}, penicillin[:sample]), convert(Vector{String}, penicillin[:plate]))   Out[3]: 6×24 Named Array{Int64,2} Dim1 ╲ Dim2 │ a b c d e f g h i j … o p q r s t u v w x ────────────┼────────────────────────────────────────────────────────────── A │ 1 1 1 1 1 1 1 1 1 1 … 1 1 1 1 1 1 1 1 1 1 B │ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 C │ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 D │ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 E │ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 F │ 1 1 1 1 1 1 1 1 1 1 … 1 1 1 1 1 1 1 1 1 1  Like the dyestuff data, the factors in the penicillin data are balanced. That is, there are exactly the same number of observations on each plate and for each sample and, furthermore, there is the same number of observations on each combination of levels. In this case there is exactly one observation for each combination of sample and plate. We would describe the configuration of these two factors as an unreplicated, completely balanced, crossed design. In general, balance is a desirable but precarious property of a data set. We may be able to impose balance in a designed experiment but we typically cannot expect that data from an observation study will be balanced. Also, as anyone who analyzes real data soon finds out, expecting that balance in the design of an experiment will produce a balanced data set is contrary to “Murphy’s Law”. That’s why statisticians allow for missing data. Even when we apply each of the six samples to each of the 24 plates, something could go wrong for one of the samples on one of the plates, leaving us without a measurement for that combination of levels and thus an unbalanced data set. ### A Model For the Penicillin Data A model incorporating random effects for both the and the is straightforward to specify — we include simple, scalar random effects terms for both these factors.  In [4]: penm = fit!(lmm(diameter ~ 1 + (1 | plate) + (1 | sample), penicillin))   Out[4]: Linear mixed model fit by maximum likelihood Formula: diameter ~ 1 + (1 | plate) + (1 | sample) logLik -2 logLik AIC BIC -166.09417 332.18835 340.18835 352.06760 Variance components: Column Variance Std.Dev. plate (Intercept) 0.71497947 0.8455646 sample (Intercept) 3.13519360 1.7706478 Residual 0.30242640 0.5499331 Number of obs: 144; levels of grouping factors: 24, 6 Fixed-effects parameters: Estimate Std.Error z value P(>|z|) (Intercept) 22.9722 0.744596 30.8519 <1e-99  This model display indicates that the sample-to-sample variability has the greatest contribution, then plate-to-plate variability and finally the “residual” variability that cannot be attributed to either the sample or the plate. These conclusions are consistent with what we see in the data plot (Fig. [fig:Penicillindot]). The prediction intervals on the random effects (Fig. [fig:fm03ranef]) [fig:fm03ranef] confirm that the conditional distribution of the random effects for has much less variability than does the conditional distribution of the random effects for , in the sense that the dots in the bottom panel have less variability than those in the top panel. (Note the different horizontal axes for the two panels.) However, the conditional distribution of the random effect for a particular , say sample F, has less variability than the conditional distribution of the random effect for a particular plate, say plate m. That is, the lines in the bottom panel are wider than the lines in the top panel, even after taking the different axis scales into account. This is because the conditional distribution of the random effect for a particular sample depends on 24 responses while the conditional distribution of the random effect for a particular plate depends on only 6 responses. In chapter [chap:ExamLMM] we saw that a model with a single, simple, scalar random-effects term generated a random-effects model matrix,$\mathbf Z$, that is the matrix of indicators of the levels of the grouping factor. When we have multiple, simple, scalar random-effects terms, as in model , each term generates a matrix of indicator columns and these sets of indicators are concatenated to form the model matrix$\mathbf Z$. The transpose of this matrix, shown in Fig. [fig:fm03Ztimage], contains rows of indicators for each factor. [fig:fm03Ztimage] [fig:fm03LambdaLimage] The relative covariance factor,$\Lambda_\theta$, (Fig. [fig:fm03LambdaLimage], left panel) is no longer a multiple of the identity. It is now block diagonal, with two blocks, one of size 24 and one of size 6, each of which is a multiple of the identity. The diagonal elements of the two blocks are$\theta_1$and$\theta_2$, respectively. The numeric values of these parameters can be obtained as  In [5]: getθ(penm)   Out[5]: 2-element Array{Float64,1}: 1.53758 3.21975  or as the Final parameter vector in the opsum field of penm  In [6]: penm.optsum   Out[6]: Initial parameter vector: [1.0,1.0] Initial objective value: 364.6267798165906 Optimizer (from NLopt): LN_BOBYQA Lower bounds: [0.0,0.0] ftol_rel: 1.0e-12 ftol_abs: 1.0e-8 xtol_rel: 0.0 xtol_abs: [1.0e-10,1.0e-10] Function evaluations: 44 Final parameter vector: [1.53758,3.21975] Final objective value: 332.1883486724448 Return code: FTOL_REACHED  The first parameter is the relative standard deviation of the random effects for plate, which has the value 0.8455646/0.5499331 = 1.53758 at convergence, and the second is the relative standard deviation of the random effects for sample (1.7706475/0.5499331 = 3.21975). Because$\Lambda_\theta$is diagonal, the pattern of non-zeros in$\Lambda_\theta'\mathbf Z'\mathbf Z\Lambda_\theta+\mathbf I$will be the same as that in$\mathbf Z'\mathbf Z$, shown in the middle panel of Fig. [fig:fm03LambdaLimage]. The sparse Cholesky factor,$\mathbf L$, shown in the right panel, is lower triangular and has non-zero elements in the lower right hand corner in positions where$\mathbf Z'\mathbf Z$has systematic zeros. We say that “fill-in” has occurred when forming the sparse Cholesky decomposition. In this case there is a relatively minor amount of fill but in other cases there can be a substantial amount of fill and we shall take precautions so as to reduce this, because fill-in adds to the computational effort in determining the MLEs or the REML estimates. A bootstrap simulation of the model  In [7]: @time penmbstp = bootstrap(10000, penm);   7.036430 seconds (14.37 M allocations: 344.560 MB, 1.76% gc time)  provides the density plots  In [8]: plot(x = penmbstp[:β₁], Geom.density(), Guide.xlabel("β₁"))   Out[8]: β₁ 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0 20.5 21.0 21.5 22.0 22.5 23.0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 28.5 29.0 29.5 30.0 30.5 31.0 31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0 0 10 20 30 40 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0 20.5 21.0 21.5 22.0 22.5 23.0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 28.5 29.0 29.5 30.0 30.5 31.0 31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 -0.62 -0.60 -0.58 -0.56 -0.54 -0.52 -0.50 -0.48 -0.46 -0.44 -0.42 -0.40 -0.38 -0.36 -0.34 -0.32 -0.30 -0.28 -0.26 -0.24 -0.22 -0.20 -0.18 -0.16 -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00 1.02 1.04 1.06 1.08 1.10 1.12 1.14 1.16 1.18 1.20 1.22 -1 0 1 2 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 (function(N){var k=/[\.\/]/,L=/\s*,\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if("undefined"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if("undefined"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)"zIndex"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&& (e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],"zIndex"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d["*"] ],n=2;n--;)if(d= f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if("function"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null, [a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp("(?:\\.|\\/|^)"+k+"(?:\\.|\\/|$)")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if("*"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&
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Element.prototype.plotroot = function () { var element = this; while (!element.hasClass("plotroot") && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.svgroot = function () { var element = this; while (element.node.nodeName != "svg" && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.plotbounds = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x0: bbox.x, x1: bbox.x + bbox.width, y0: bbox.y, y1: bbox.y + bbox.height }; }; Element.prototype.plotcenter = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x: bbox.x + bbox.width / 2, y: bbox.y + bbox.height / 2 }; }; // Emulate IE style mouseenter/mouseleave events, since Microsoft always // does everything right. // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/ var events = ["mouseenter", "mouseleave"]; for (i in events) { (function (event_name) { var event_name = events[i]; Element.prototype[event_name] = function (fn, scope) { if (Snap.is(fn, "function")) { var fn2 = function (event) { if (event.type != "mouseover" && event.type != "mouseout") { return; } var reltg = event.relatedTarget ? event.relatedTarget : event.type == "mouseout" ? event.toElement : event.fromElement; while (reltg && reltg != this.node) reltg = reltg.parentNode; if (reltg != this.node) { return fn.apply(this, event); } }; if (event_name == "mouseenter") { this.mouseover(fn2, scope); } else { this.mouseout(fn2, scope); } } return this; }; })(events[i]); } Element.prototype.mousewheel = function (fn, scope) { if (Snap.is(fn, "function")) { var el = this; var fn2 = function (event) { fn.apply(el, [event]); }; } this.node.addEventListener( /Firefox/i.test(navigator.userAgent) ? "DOMMouseScroll" : "mousewheel", fn2); return this; }; // Snap's attr function can be too slow for things like panning/zooming. // This is a function to directly update element attributes without going // through eve. Element.prototype.attribute = function(key, val) { if (val === undefined) { return this.node.getAttribute(key); } else { this.node.setAttribute(key, val); return this; } }; Element.prototype.init_gadfly = function() { this.mouseenter(Gadfly.plot_mouseover) .mouseleave(Gadfly.plot_mouseout) .dblclick(Gadfly.plot_dblclick) .mousewheel(Gadfly.guide_background_scroll) .drag(Gadfly.guide_background_drag_onmove, Gadfly.guide_background_drag_onstart, Gadfly.guide_background_drag_onend); this.mouseenter(function (event) { init_pan_zoom(this.plotroot()); }); return this; }; }); // When the plot is moused over, emphasize the grid lines. Gadfly.plot_mouseover = function(event) { var root = this.plotroot(); var keyboard_zoom = function(event) { if (event.which == 187) { // plus increase_zoom_by_position(root, 0.1, true); } else if (event.which == 189) { // minus increase_zoom_by_position(root, -0.1, true); } }; root.data("keyboard_zoom", keyboard_zoom); window.addEventListener("keyup", keyboard_zoom); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); xgridlines.data("unfocused_strokedash", xgridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
ygridlines.data("unfocused_strokedash",
ygridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2")); // emphasize grid lines var destcolor = root.data("focused_xgrid_color"); xgridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("focused_ygrid_color"); ygridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); // reveal zoom slider root.select(".zoomslider") .animate({opacity: 1.0}, 250); }; // Reset pan and zoom on double click Gadfly.plot_dblclick = function(event) { set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0); }; // Unemphasize grid lines on mouse out. Gadfly.plot_mouseout = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("keyboard_zoom")); root.data("keyboard_zoom", undefined); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); var destcolor = root.data("unfocused_xgrid_color"); xgridlines.attribute("stroke-dasharray", xgridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("unfocused_ygrid_color"); ygridlines.attribute("stroke-dasharray", ygridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); // hide zoom slider root.select(".zoomslider") .animate({opacity: 0.0}, 250); }; var set_geometry_transform = function(root, tx, ty, scale) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var old_scale = root.data("scale"); var xscale = xscalable ? scale : 1.0, yscale = yscalable ? scale : 1.0; tx = xscalable ? tx : 0.0; ty = yscalable ? ty : 0.0; var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale); root.selectAll(".geometry, image") .forEach(function (element, i) { element.transform(t); }); bounds = root.plotbounds(); if (yscalable) { var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale); root.selectAll(".xfixed") .forEach(function (element, i) { element.transform(xfixed_t); }); root.select(".ylabels") .transform(xfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1, 1/scale, cx, cy).add(st); element.transform(unscale_t); var y = cy * scale + ty; element.attr("visibility", bounds.y0 <= y && y <= bounds.y1 ? "visible" : "hidden"); } }); } if (xscalable) { var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0); var xtrans = new Snap.Matrix().translate(tx, 0); root.selectAll(".yfixed") .forEach(function (element, i) { element.transform(yfixed_t); }); root.select(".xlabels") .transform(yfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1/scale, 1, cx, cy).add(st); element.transform(unscale_t); var x = cx * scale + tx; element.attr("visibility", bounds.x0 <= x && x <= bounds.x1 ? "visible" : "hidden"); } }); } // we must unscale anything that is scale invariance: widths, raiduses, etc. var size_attribs = ["font-size"]; var unscaled_selection = ".geometry, .geometry *"; if (xscalable) { size_attribs.push("rx"); unscaled_selection += ", .xgridlines"; } if (yscalable) { size_attribs.push("ry"); unscaled_selection += ", .ygridlines"; } root.selectAll(unscaled_selection) .forEach(function (element, i) { // circle need special help if (element.node.nodeName == "circle") { var cx = element.attribute("cx"), cy = element.attribute("cy"); unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale, cx, cy); element.transform(unscale_t); return; } for (i in size_attribs) { var key = size_attribs[i]; var val = parseFloat(element.attribute(key)); if (val !== undefined && val != 0 && !isNaN(val)) { element.attribute(key, val * old_scale / scale); } } }); }; // Find the most appropriate tick scale and update label visibility. var update_tickscale = function(root, scale, axis) { if (!root.hasClass(axis + "scalable")) return; var tickscales = root.data(axis + "tickscales"); var best_tickscale = 1.0; var best_tickscale_dist = Infinity; for (tickscale in tickscales) { var dist = Math.abs(Math.log(tickscale) - Math.log(scale)); if (dist < best_tickscale_dist) { best_tickscale_dist = dist; best_tickscale = tickscale; } } if (best_tickscale != root.data(axis + "tickscale")) { root.data(axis + "tickscale", best_tickscale); var mark_inscale_gridlines = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; var mark_inscale_labels = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; root.select("." + axis + "gridlines").selectAll("path").forEach(mark_inscale_gridlines); root.select("." + axis + "labels").selectAll("text").forEach(mark_inscale_labels); } }; var set_plot_pan_zoom = function(root, tx, ty, scale) { var old_scale = root.data("scale"); var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; // compute the viewport derived from tx, ty, and scale var x_min = -width * scale - (scale * width - width), x_max = width * scale, y_min = -height * scale - (scale * height - height), y_max = height * scale; var x0 = bounds.x0 - scale * bounds.x0, y0 = bounds.y0 - scale * bounds.y0; var tx = Math.max(Math.min(tx - x0, x_max), x_min), ty = Math.max(Math.min(ty - y0, y_max), y_min); tx += x0; ty += y0; // when the scale change, we may need to alter which set of // ticks is being displayed if (scale != old_scale) { update_tickscale(root, scale, "x"); update_tickscale(root, scale, "y"); } set_geometry_transform(root, tx, ty, scale); root.data("scale", scale); root.data("tx", tx); root.data("ty", ty); }; var scale_centered_translation = function(root, scale) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var tx0 = root.data("tx"), ty0 = root.data("ty"); var scale0 = root.data("scale"); // how off from center the current view is var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2), yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2); // rescale offsets xoff = xoff * scale / scale0; yoff = yoff * scale / scale0; // adjust for the panel position being scaled var x_edge_adjust = bounds.x0 * (1 - scale), y_edge_adjust = bounds.y0 * (1 - scale); return { x: xoff + x_edge_adjust + (width - width * scale) / 2, y: yoff + y_edge_adjust + (height - height * scale) / 2 }; }; // Initialize data for panning zooming if it isn't already. var init_pan_zoom = function(root) { if (root.data("zoompan-ready")) { return; } // The non-scaling-stroke trick. Rather than try to correct for the // stroke-width when zooming, we force it to a fixed value. var px_per_mm = root.node.getCTM().a; // Drag events report deltas in pixels, which we'd like to convert to // millimeters. root.data("px_per_mm", px_per_mm); root.selectAll("path") .forEach(function (element, i) { sw = element.asPX("stroke-width") * px_per_mm; if (sw > 0) { element.attribute("stroke-width", sw); element.attribute("vector-effect", "non-scaling-stroke"); } }); // Store ticks labels original tranformation root.selectAll(".xlabels > text, .ylabels > text") .forEach(function (element, i) { var lm = element.transform().localMatrix; element.data("static_transform", new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f)); }); var xgridlines = root.select(".xgridlines"); var ygridlines = root.select(".ygridlines"); var xlabels = root.select(".xlabels"); var ylabels = root.select(".ylabels"); if (root.data("tx") === undefined) root.data("tx", 0); if (root.data("ty") === undefined) root.data("ty", 0); if (root.data("scale") === undefined) root.data("scale", 1.0); if (root.data("xtickscales") === undefined) { // index all the tick scales that are listed var xtickscales = {}; var ytickscales = {}; var add_x_tick_scales = function (element, i) { xtickscales[element.attribute("gadfly:scale")] = true; }; var add_y_tick_scales = function (element, i) { ytickscales[element.attribute("gadfly:scale")] = true; }; if (xgridlines) xgridlines.selectAll("path").forEach(add_x_tick_scales); if (ygridlines) ygridlines.selectAll("path").forEach(add_y_tick_scales); if (xlabels) xlabels.selectAll("text").forEach(add_x_tick_scales); if (ylabels) ylabels.selectAll("text").forEach(add_y_tick_scales); root.data("xtickscales", xtickscales); root.data("ytickscales", ytickscales); root.data("xtickscale", 1.0); } var min_scale = 1.0, max_scale = 1.0; for (scale in xtickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } for (scale in ytickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } root.data("min_scale", min_scale); root.data("max_scale", max_scale); // store the original positions of labels if (xlabels) { xlabels.selectAll("text") .forEach(function (element, i) { element.data("x", element.asPX("x")); }); } if (ylabels) { ylabels.selectAll("text") .forEach(function (element, i) { element.data("y", element.asPX("y")); }); } // mark grid lines and ticks as in or out of scale. var mark_inscale = function (element, i) { element.attribute("gadfly:inscale", element.attribute("gadfly:scale") == 1.0); }; if (xgridlines) xgridlines.selectAll("path").forEach(mark_inscale); if (ygridlines) ygridlines.selectAll("path").forEach(mark_inscale); if (xlabels) xlabels.selectAll("text").forEach(mark_inscale); if (ylabels) ylabels.selectAll("text").forEach(mark_inscale); // figure out the upper ond lower bounds on panning using the maximum // and minum grid lines var bounds = root.plotbounds(); var pan_bounds = { x0: 0.0, y0: 0.0, x1: 0.0, y1: 0.0 }; if (xgridlines) { xgridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.x1 - bbox.x < pan_bounds.x0) { pan_bounds.x0 = bounds.x1 - bbox.x; } if (bounds.x0 - bbox.x > pan_bounds.x1) { pan_bounds.x1 = bounds.x0 - bbox.x; } element.attr("visibility", "visible"); } }); } if (ygridlines) { ygridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.y1 - bbox.y < pan_bounds.y0) { pan_bounds.y0 = bounds.y1 - bbox.y; } if (bounds.y0 - bbox.y > pan_bounds.y1) { pan_bounds.y1 = bounds.y0 - bbox.y; } element.attr("visibility", "visible"); } }); } // nudge these values a little pan_bounds.x0 -= 5; pan_bounds.x1 += 5; pan_bounds.y0 -= 5; pan_bounds.y1 += 5; root.data("pan_bounds", pan_bounds); root.data("zoompan-ready", true) }; // drag actions, i.e. zooming and panning var pan_action = { start: function(root, x, y, event) { root.data("dx", 0); root.data("dy", 0); root.data("tx0", root.data("tx")); root.data("ty0", root.data("ty")); }, update: function(root, dx, dy, x, y, event) { var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var tx0 = root.data("tx"), ty0 = root.data("ty"); var dx0 = root.data("dx"), dy0 = root.data("dy"); root.data("dx", dx); root.data("dy", dy); dx = dx - dx0; dy = dy - dy0; var tx = tx0 + dx, ty = ty0 + dy; set_plot_pan_zoom(root, tx, ty, root.data("scale")); }, end: function(root, event) { }, cancel: function(root) { set_plot_pan_zoom(root, root.data("tx0"), root.data("ty0"), root.data("scale")); } }; var zoom_box; var zoom_action = { start: function(root, x, y, event) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var ratio = width / height; var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); x = xscalable ? x / px_per_mm : bounds.x0; y = yscalable ? y / px_per_mm : bounds.y0; var w = xscalable ? 0 : width; var h = yscalable ? 0 : height; zoom_box = root.rect(x, y, w, h).attr({ "fill": "#000", "opacity": 0.25 }); zoom_box.data("ratio", ratio); }, update: function(root, dx, dy, x, y, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); var bounds = root.plotbounds(); if (yscalable) { y /= px_per_mm; y = Math.max(bounds.y0, y); y = Math.min(bounds.y1, y); } else { y = bounds.y1; } if (xscalable) { x /= px_per_mm; x = Math.max(bounds.x0, x); x = Math.min(bounds.x1, x); } else { x = bounds.x1; } dx = x - zoom_box.attr("x"); dy = y - zoom_box.attr("y"); if (xscalable && yscalable) { var ratio = zoom_box.data("ratio"); var width = Math.min(Math.abs(dx), ratio * Math.abs(dy)); var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio); dx = width * dx / Math.abs(dx); dy = height * dy / Math.abs(dy); } var xoffset = 0, yoffset = 0; if (dx < 0) { xoffset = dx; dx = -1 * dx; } if (dy < 0) { yoffset = dy; dy = -1 * dy; } if (isNaN(dy)) { dy = 0.0; } if (isNaN(dx)) { dx = 0.0; } zoom_box.transform("T" + xoffset + "," + yoffset); zoom_box.attr("width", dx); zoom_box.attr("height", dy); }, end: function(root, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var zoom_bounds = zoom_box.getBBox(); if (zoom_bounds.width * zoom_bounds.height <= 0) { return; } var plot_bounds = root.plotbounds(); var zoom_factor = 1.0; if (yscalable) { zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height; } else { zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width; } var tx = (root.data("tx") - zoom_bounds.x) * zoom_factor + plot_bounds.x0, ty = (root.data("ty") - zoom_bounds.y) * zoom_factor + plot_bounds.y0; set_plot_pan_zoom(root, tx, ty, root.data("scale") * zoom_factor); zoom_box.remove(); }, cancel: function(root) { zoom_box.remove(); } }; Gadfly.guide_background_drag_onstart = function(x, y, event) { var root = this.plotroot(); var scalable = root.hasClass("xscalable") || root.hasClass("yscalable"); var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable; var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable; var drag_action = zoomable ? zoom_action : panable ? pan_action : undefined; root.data("drag_action", drag_action); if (drag_action) { var cancel_drag_action = function(event) { if (event.which == 27) { // esc key drag_action.cancel(root); root.data("drag_action", undefined); } }; window.addEventListener("keyup", cancel_drag_action); root.data("cancel_drag_action", cancel_drag_action); drag_action.start(root, x, y, event); } }; Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.update(root, dx, dy, x, y, event); } }; Gadfly.guide_background_drag_onend = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("cancel_drag_action")); root.data("cancel_drag_action", undefined); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.end(root, event); } root.data("drag_action", undefined); }; Gadfly.guide_background_scroll = function(event) { if (event.shiftKey) { increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta); event.preventDefault(); } }; Gadfly.zoomslider_button_mouseover = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseover_color")}, 100); }; Gadfly.zoomslider_button_mouseout = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseout_color")}, 100); }; Gadfly.zoomslider_zoomout_click = function(event) { increase_zoom_by_position(this.plotroot(), -0.1, true); }; Gadfly.zoomslider_zoomin_click = function(event) { increase_zoom_by_position(this.plotroot(), 0.1, true); }; Gadfly.zoomslider_track_click = function(event) { // TODO }; // Map slider position x to scale y using the function y = a*exp(b*x)+c. // The constants a, b, and c are solved using the constraint that the function // should go through the points (0; min_scale), (0.5; 1), and (1; max_scale). var scale_from_slider_position = function(position, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return a * Math.exp(b * position) + c; } // inverse of scale_from_slider_position var slider_position_from_scale = function(scale, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return 1 / b * Math.log((scale - c) / a); } var increase_zoom_by_position = function(root, delta_position, animate) { var scale = root.data("scale"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var position = slider_position_from_scale(scale, min_scale, max_scale); position += delta_position; scale = scale_from_slider_position(position, min_scale, max_scale); set_zoom(root, scale, animate); } var set_zoom = function(root, scale, animate) { var min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("scale"); var new_scale = Math.max(min_scale, Math.min(scale, max_scale)); if (animate) { Snap.animate( old_scale, new_scale, function (new_scale) { update_plot_scale(root, new_scale); }, 200); } else { update_plot_scale(root, new_scale); } } var update_plot_scale = function(root, new_scale) { var trans = scale_centered_translation(root, new_scale); set_plot_pan_zoom(root, trans.x, trans.y, new_scale); root.selectAll(".zoomslider_thumb") .forEach(function (element, i) { var min_pos = element.data("min_pos"), max_pos = element.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(new_scale, min_scale, max_scale); element.transform(new Snap.Matrix().translate( Math.max(min_pos, Math.min( max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0)); }); }; Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var min_pos = this.data("min_pos"), max_pos = this.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("old_scale"); var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) + dx / (max_pos - min_pos); // compute the new scale var new_scale = scale_from_slider_position(xpos, min_scale, max_scale); new_scale = Math.min(max_scale, Math.max(min_scale, new_scale)); update_plot_scale(root, new_scale); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragstart = function(x, y, event) { this.animate({fill: this.data("mouseover_color")}, 100); var root = this.plotroot(); // keep track of what the scale was when we started dragging root.data("old_scale", root.data("scale")); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragend = function(event) { this.animate({fill: this.data("mouseout_color")}, 100); event.stopPropagation(); }; var toggle_color_class = function(root, color_class, ison) { var guides = root.selectAll(".guide." + color_class + ",.guide ." + color_class); var geoms = root.selectAll(".geometry." + color_class + ",.geometry ." + color_class); if (ison) { guides.animate({opacity: 0.5}, 250); geoms.animate({opacity: 0.0}, 250); } else { guides.animate({opacity: 1.0}, 250); geoms.animate({opacity: 1.0}, 250); } }; Gadfly.colorkey_swatch_click = function(event) { var root = this.plotroot(); var color_class = this.data("color_class"); if (event.shiftKey) { root.selectAll(".colorkey text") .forEach(function (element) { var other_color_class = element.data("color_class"); if (other_color_class != color_class) { toggle_color_class(root, other_color_class, element.attr("opacity") == 1.0); } }); } else { toggle_color_class(root, color_class, this.attr("opacity") == 1.0); } }; return Gadfly; })); //@ sourceURL=gadfly.js (function (glob, factory) { // AMD support if (typeof require === "function" && typeof define === "function" && define.amd) { require(["Snap.svg", "Gadfly"], function (Snap, Gadfly) { factory(Snap, Gadfly); }); } else { factory(glob.Snap, glob.Gadfly); } })(window, function (Snap, Gadfly) { var fig = Snap("#img-2e276d5c"); fig.select("#img-2e276d5c-5") .init_gadfly(); fig.select("#img-2e276d5c-7") .plotroot().data("unfocused_ygrid_color", "#D0D0E0") ; fig.select("#img-2e276d5c-7") .plotroot().data("focused_ygrid_color", "#A0A0A0") ; fig.select("#img-2e276d5c-8") .plotroot().data("unfocused_xgrid_color", "#D0D0E0") ; fig.select("#img-2e276d5c-8") .plotroot().data("focused_xgrid_color", "#A0A0A0") ; fig.select("#img-2e276d5c-12") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-2e276d5c-12") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-2e276d5c-12") .click(Gadfly.zoomslider_zoomin_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; fig.select("#img-2e276d5c-14") .data("max_pos", 120.42) ; fig.select("#img-2e276d5c-14") .data("min_pos", 103.42) ; fig.select("#img-2e276d5c-14") .click(Gadfly.zoomslider_track_click); fig.select("#img-2e276d5c-15") .data("max_pos", 120.42) ; fig.select("#img-2e276d5c-15") .data("min_pos", 103.42) ; fig.select("#img-2e276d5c-15") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-2e276d5c-15") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-2e276d5c-15") .drag(Gadfly.zoomslider_thumb_dragmove, Gadfly.zoomslider_thumb_dragstart, Gadfly.zoomslider_thumb_dragend) ; fig.select("#img-2e276d5c-16") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-2e276d5c-16") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-2e276d5c-16") .click(Gadfly.zoomslider_zoomout_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; });   In [9]: plot(x = penmbstp[:σ], Geom.density(), Guide.xlabel("σ"))   Out[9]: σ -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 -0.20 -0.18 -0.16 -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00 1.02 1.04 1.06 1.08 1.10 1.12 1.14 1.16 1.18 1.20 1.22 1.24 1.26 1.28 1.30 1.32 -0.5 0.0 0.5 1.0 1.5 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 -20 -15 -10 -5 0 5 10 15 20 25 30 35 -15.0 -14.5 -14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -7.0 -6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0 20.5 21.0 21.5 22.0 22.5 23.0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 28.5 29.0 29.5 30.0 -20 0 20 40 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 (function(N){var k=/[\.\/]/,L=/\s*,\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if("undefined"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if("undefined"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)"zIndex"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&& (e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],"zIndex"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d["*"] ],n=2;n--;)if(d= f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if("function"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null, [a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp("(?:\\.|\\/|^)"+k+"(?:\\.|\\/|$)")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if("*"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&
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Element.prototype.plotroot = function () { var element = this; while (!element.hasClass("plotroot") && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.svgroot = function () { var element = this; while (element.node.nodeName != "svg" && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.plotbounds = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x0: bbox.x, x1: bbox.x + bbox.width, y0: bbox.y, y1: bbox.y + bbox.height }; }; Element.prototype.plotcenter = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x: bbox.x + bbox.width / 2, y: bbox.y + bbox.height / 2 }; }; // Emulate IE style mouseenter/mouseleave events, since Microsoft always // does everything right. // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/ var events = ["mouseenter", "mouseleave"]; for (i in events) { (function (event_name) { var event_name = events[i]; Element.prototype[event_name] = function (fn, scope) { if (Snap.is(fn, "function")) { var fn2 = function (event) { if (event.type != "mouseover" && event.type != "mouseout") { return; } var reltg = event.relatedTarget ? event.relatedTarget : event.type == "mouseout" ? event.toElement : event.fromElement; while (reltg && reltg != this.node) reltg = reltg.parentNode; if (reltg != this.node) { return fn.apply(this, event); } }; if (event_name == "mouseenter") { this.mouseover(fn2, scope); } else { this.mouseout(fn2, scope); } } return this; }; })(events[i]); } Element.prototype.mousewheel = function (fn, scope) { if (Snap.is(fn, "function")) { var el = this; var fn2 = function (event) { fn.apply(el, [event]); }; } this.node.addEventListener( /Firefox/i.test(navigator.userAgent) ? "DOMMouseScroll" : "mousewheel", fn2); return this; }; // Snap's attr function can be too slow for things like panning/zooming. // This is a function to directly update element attributes without going // through eve. Element.prototype.attribute = function(key, val) { if (val === undefined) { return this.node.getAttribute(key); } else { this.node.setAttribute(key, val); return this; } }; Element.prototype.init_gadfly = function() { this.mouseenter(Gadfly.plot_mouseover) .mouseleave(Gadfly.plot_mouseout) .dblclick(Gadfly.plot_dblclick) .mousewheel(Gadfly.guide_background_scroll) .drag(Gadfly.guide_background_drag_onmove, Gadfly.guide_background_drag_onstart, Gadfly.guide_background_drag_onend); this.mouseenter(function (event) { init_pan_zoom(this.plotroot()); }); return this; }; }); // When the plot is moused over, emphasize the grid lines. Gadfly.plot_mouseover = function(event) { var root = this.plotroot(); var keyboard_zoom = function(event) { if (event.which == 187) { // plus increase_zoom_by_position(root, 0.1, true); } else if (event.which == 189) { // minus increase_zoom_by_position(root, -0.1, true); } }; root.data("keyboard_zoom", keyboard_zoom); window.addEventListener("keyup", keyboard_zoom); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); xgridlines.data("unfocused_strokedash", xgridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
ygridlines.data("unfocused_strokedash",
ygridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2")); // emphasize grid lines var destcolor = root.data("focused_xgrid_color"); xgridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("focused_ygrid_color"); ygridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); // reveal zoom slider root.select(".zoomslider") .animate({opacity: 1.0}, 250); }; // Reset pan and zoom on double click Gadfly.plot_dblclick = function(event) { set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0); }; // Unemphasize grid lines on mouse out. Gadfly.plot_mouseout = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("keyboard_zoom")); root.data("keyboard_zoom", undefined); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); var destcolor = root.data("unfocused_xgrid_color"); xgridlines.attribute("stroke-dasharray", xgridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("unfocused_ygrid_color"); ygridlines.attribute("stroke-dasharray", ygridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); // hide zoom slider root.select(".zoomslider") .animate({opacity: 0.0}, 250); }; var set_geometry_transform = function(root, tx, ty, scale) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var old_scale = root.data("scale"); var xscale = xscalable ? scale : 1.0, yscale = yscalable ? scale : 1.0; tx = xscalable ? tx : 0.0; ty = yscalable ? ty : 0.0; var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale); root.selectAll(".geometry, image") .forEach(function (element, i) { element.transform(t); }); bounds = root.plotbounds(); if (yscalable) { var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale); root.selectAll(".xfixed") .forEach(function (element, i) { element.transform(xfixed_t); }); root.select(".ylabels") .transform(xfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1, 1/scale, cx, cy).add(st); element.transform(unscale_t); var y = cy * scale + ty; element.attr("visibility", bounds.y0 <= y && y <= bounds.y1 ? "visible" : "hidden"); } }); } if (xscalable) { var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0); var xtrans = new Snap.Matrix().translate(tx, 0); root.selectAll(".yfixed") .forEach(function (element, i) { element.transform(yfixed_t); }); root.select(".xlabels") .transform(yfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1/scale, 1, cx, cy).add(st); element.transform(unscale_t); var x = cx * scale + tx; element.attr("visibility", bounds.x0 <= x && x <= bounds.x1 ? "visible" : "hidden"); } }); } // we must unscale anything that is scale invariance: widths, raiduses, etc. var size_attribs = ["font-size"]; var unscaled_selection = ".geometry, .geometry *"; if (xscalable) { size_attribs.push("rx"); unscaled_selection += ", .xgridlines"; } if (yscalable) { size_attribs.push("ry"); unscaled_selection += ", .ygridlines"; } root.selectAll(unscaled_selection) .forEach(function (element, i) { // circle need special help if (element.node.nodeName == "circle") { var cx = element.attribute("cx"), cy = element.attribute("cy"); unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale, cx, cy); element.transform(unscale_t); return; } for (i in size_attribs) { var key = size_attribs[i]; var val = parseFloat(element.attribute(key)); if (val !== undefined && val != 0 && !isNaN(val)) { element.attribute(key, val * old_scale / scale); } } }); }; // Find the most appropriate tick scale and update label visibility. var update_tickscale = function(root, scale, axis) { if (!root.hasClass(axis + "scalable")) return; var tickscales = root.data(axis + "tickscales"); var best_tickscale = 1.0; var best_tickscale_dist = Infinity; for (tickscale in tickscales) { var dist = Math.abs(Math.log(tickscale) - Math.log(scale)); if (dist < best_tickscale_dist) { best_tickscale_dist = dist; best_tickscale = tickscale; } } if (best_tickscale != root.data(axis + "tickscale")) { root.data(axis + "tickscale", best_tickscale); var mark_inscale_gridlines = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; var mark_inscale_labels = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; root.select("." + axis + "gridlines").selectAll("path").forEach(mark_inscale_gridlines); root.select("." + axis + "labels").selectAll("text").forEach(mark_inscale_labels); } }; var set_plot_pan_zoom = function(root, tx, ty, scale) { var old_scale = root.data("scale"); var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; // compute the viewport derived from tx, ty, and scale var x_min = -width * scale - (scale * width - width), x_max = width * scale, y_min = -height * scale - (scale * height - height), y_max = height * scale; var x0 = bounds.x0 - scale * bounds.x0, y0 = bounds.y0 - scale * bounds.y0; var tx = Math.max(Math.min(tx - x0, x_max), x_min), ty = Math.max(Math.min(ty - y0, y_max), y_min); tx += x0; ty += y0; // when the scale change, we may need to alter which set of // ticks is being displayed if (scale != old_scale) { update_tickscale(root, scale, "x"); update_tickscale(root, scale, "y"); } set_geometry_transform(root, tx, ty, scale); root.data("scale", scale); root.data("tx", tx); root.data("ty", ty); }; var scale_centered_translation = function(root, scale) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var tx0 = root.data("tx"), ty0 = root.data("ty"); var scale0 = root.data("scale"); // how off from center the current view is var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2), yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2); // rescale offsets xoff = xoff * scale / scale0; yoff = yoff * scale / scale0; // adjust for the panel position being scaled var x_edge_adjust = bounds.x0 * (1 - scale), y_edge_adjust = bounds.y0 * (1 - scale); return { x: xoff + x_edge_adjust + (width - width * scale) / 2, y: yoff + y_edge_adjust + (height - height * scale) / 2 }; }; // Initialize data for panning zooming if it isn't already. var init_pan_zoom = function(root) { if (root.data("zoompan-ready")) { return; } // The non-scaling-stroke trick. Rather than try to correct for the // stroke-width when zooming, we force it to a fixed value. var px_per_mm = root.node.getCTM().a; // Drag events report deltas in pixels, which we'd like to convert to // millimeters. root.data("px_per_mm", px_per_mm); root.selectAll("path") .forEach(function (element, i) { sw = element.asPX("stroke-width") * px_per_mm; if (sw > 0) { element.attribute("stroke-width", sw); element.attribute("vector-effect", "non-scaling-stroke"); } }); // Store ticks labels original tranformation root.selectAll(".xlabels > text, .ylabels > text") .forEach(function (element, i) { var lm = element.transform().localMatrix; element.data("static_transform", new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f)); }); var xgridlines = root.select(".xgridlines"); var ygridlines = root.select(".ygridlines"); var xlabels = root.select(".xlabels"); var ylabels = root.select(".ylabels"); if (root.data("tx") === undefined) root.data("tx", 0); if (root.data("ty") === undefined) root.data("ty", 0); if (root.data("scale") === undefined) root.data("scale", 1.0); if (root.data("xtickscales") === undefined) { // index all the tick scales that are listed var xtickscales = {}; var ytickscales = {}; var add_x_tick_scales = function (element, i) { xtickscales[element.attribute("gadfly:scale")] = true; }; var add_y_tick_scales = function (element, i) { ytickscales[element.attribute("gadfly:scale")] = true; }; if (xgridlines) xgridlines.selectAll("path").forEach(add_x_tick_scales); if (ygridlines) ygridlines.selectAll("path").forEach(add_y_tick_scales); if (xlabels) xlabels.selectAll("text").forEach(add_x_tick_scales); if (ylabels) ylabels.selectAll("text").forEach(add_y_tick_scales); root.data("xtickscales", xtickscales); root.data("ytickscales", ytickscales); root.data("xtickscale", 1.0); } var min_scale = 1.0, max_scale = 1.0; for (scale in xtickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } for (scale in ytickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } root.data("min_scale", min_scale); root.data("max_scale", max_scale); // store the original positions of labels if (xlabels) { xlabels.selectAll("text") .forEach(function (element, i) { element.data("x", element.asPX("x")); }); } if (ylabels) { ylabels.selectAll("text") .forEach(function (element, i) { element.data("y", element.asPX("y")); }); } // mark grid lines and ticks as in or out of scale. var mark_inscale = function (element, i) { element.attribute("gadfly:inscale", element.attribute("gadfly:scale") == 1.0); }; if (xgridlines) xgridlines.selectAll("path").forEach(mark_inscale); if (ygridlines) ygridlines.selectAll("path").forEach(mark_inscale); if (xlabels) xlabels.selectAll("text").forEach(mark_inscale); if (ylabels) ylabels.selectAll("text").forEach(mark_inscale); // figure out the upper ond lower bounds on panning using the maximum // and minum grid lines var bounds = root.plotbounds(); var pan_bounds = { x0: 0.0, y0: 0.0, x1: 0.0, y1: 0.0 }; if (xgridlines) { xgridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.x1 - bbox.x < pan_bounds.x0) { pan_bounds.x0 = bounds.x1 - bbox.x; } if (bounds.x0 - bbox.x > pan_bounds.x1) { pan_bounds.x1 = bounds.x0 - bbox.x; } element.attr("visibility", "visible"); } }); } if (ygridlines) { ygridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.y1 - bbox.y < pan_bounds.y0) { pan_bounds.y0 = bounds.y1 - bbox.y; } if (bounds.y0 - bbox.y > pan_bounds.y1) { pan_bounds.y1 = bounds.y0 - bbox.y; } element.attr("visibility", "visible"); } }); } // nudge these values a little pan_bounds.x0 -= 5; pan_bounds.x1 += 5; pan_bounds.y0 -= 5; pan_bounds.y1 += 5; root.data("pan_bounds", pan_bounds); root.data("zoompan-ready", true) }; // drag actions, i.e. zooming and panning var pan_action = { start: function(root, x, y, event) { root.data("dx", 0); root.data("dy", 0); root.data("tx0", root.data("tx")); root.data("ty0", root.data("ty")); }, update: function(root, dx, dy, x, y, event) { var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var tx0 = root.data("tx"), ty0 = root.data("ty"); var dx0 = root.data("dx"), dy0 = root.data("dy"); root.data("dx", dx); root.data("dy", dy); dx = dx - dx0; dy = dy - dy0; var tx = tx0 + dx, ty = ty0 + dy; set_plot_pan_zoom(root, tx, ty, root.data("scale")); }, end: function(root, event) { }, cancel: function(root) { set_plot_pan_zoom(root, root.data("tx0"), root.data("ty0"), root.data("scale")); } }; var zoom_box; var zoom_action = { start: function(root, x, y, event) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var ratio = width / height; var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); x = xscalable ? x / px_per_mm : bounds.x0; y = yscalable ? y / px_per_mm : bounds.y0; var w = xscalable ? 0 : width; var h = yscalable ? 0 : height; zoom_box = root.rect(x, y, w, h).attr({ "fill": "#000", "opacity": 0.25 }); zoom_box.data("ratio", ratio); }, update: function(root, dx, dy, x, y, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); var bounds = root.plotbounds(); if (yscalable) { y /= px_per_mm; y = Math.max(bounds.y0, y); y = Math.min(bounds.y1, y); } else { y = bounds.y1; } if (xscalable) { x /= px_per_mm; x = Math.max(bounds.x0, x); x = Math.min(bounds.x1, x); } else { x = bounds.x1; } dx = x - zoom_box.attr("x"); dy = y - zoom_box.attr("y"); if (xscalable && yscalable) { var ratio = zoom_box.data("ratio"); var width = Math.min(Math.abs(dx), ratio * Math.abs(dy)); var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio); dx = width * dx / Math.abs(dx); dy = height * dy / Math.abs(dy); } var xoffset = 0, yoffset = 0; if (dx < 0) { xoffset = dx; dx = -1 * dx; } if (dy < 0) { yoffset = dy; dy = -1 * dy; } if (isNaN(dy)) { dy = 0.0; } if (isNaN(dx)) { dx = 0.0; } zoom_box.transform("T" + xoffset + "," + yoffset); zoom_box.attr("width", dx); zoom_box.attr("height", dy); }, end: function(root, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var zoom_bounds = zoom_box.getBBox(); if (zoom_bounds.width * zoom_bounds.height <= 0) { return; } var plot_bounds = root.plotbounds(); var zoom_factor = 1.0; if (yscalable) { zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height; } else { zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width; } var tx = (root.data("tx") - zoom_bounds.x) * zoom_factor + plot_bounds.x0, ty = (root.data("ty") - zoom_bounds.y) * zoom_factor + plot_bounds.y0; set_plot_pan_zoom(root, tx, ty, root.data("scale") * zoom_factor); zoom_box.remove(); }, cancel: function(root) { zoom_box.remove(); } }; Gadfly.guide_background_drag_onstart = function(x, y, event) { var root = this.plotroot(); var scalable = root.hasClass("xscalable") || root.hasClass("yscalable"); var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable; var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable; var drag_action = zoomable ? zoom_action : panable ? pan_action : undefined; root.data("drag_action", drag_action); if (drag_action) { var cancel_drag_action = function(event) { if (event.which == 27) { // esc key drag_action.cancel(root); root.data("drag_action", undefined); } }; window.addEventListener("keyup", cancel_drag_action); root.data("cancel_drag_action", cancel_drag_action); drag_action.start(root, x, y, event); } }; Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.update(root, dx, dy, x, y, event); } }; Gadfly.guide_background_drag_onend = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("cancel_drag_action")); root.data("cancel_drag_action", undefined); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.end(root, event); } root.data("drag_action", undefined); }; Gadfly.guide_background_scroll = function(event) { if (event.shiftKey) { increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta); event.preventDefault(); } }; Gadfly.zoomslider_button_mouseover = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseover_color")}, 100); }; Gadfly.zoomslider_button_mouseout = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseout_color")}, 100); }; Gadfly.zoomslider_zoomout_click = function(event) { increase_zoom_by_position(this.plotroot(), -0.1, true); }; Gadfly.zoomslider_zoomin_click = function(event) { increase_zoom_by_position(this.plotroot(), 0.1, true); }; Gadfly.zoomslider_track_click = function(event) { // TODO }; // Map slider position x to scale y using the function y = a*exp(b*x)+c. // The constants a, b, and c are solved using the constraint that the function // should go through the points (0; min_scale), (0.5; 1), and (1; max_scale). var scale_from_slider_position = function(position, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return a * Math.exp(b * position) + c; } // inverse of scale_from_slider_position var slider_position_from_scale = function(scale, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return 1 / b * Math.log((scale - c) / a); } var increase_zoom_by_position = function(root, delta_position, animate) { var scale = root.data("scale"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var position = slider_position_from_scale(scale, min_scale, max_scale); position += delta_position; scale = scale_from_slider_position(position, min_scale, max_scale); set_zoom(root, scale, animate); } var set_zoom = function(root, scale, animate) { var min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("scale"); var new_scale = Math.max(min_scale, Math.min(scale, max_scale)); if (animate) { Snap.animate( old_scale, new_scale, function (new_scale) { update_plot_scale(root, new_scale); }, 200); } else { update_plot_scale(root, new_scale); } } var update_plot_scale = function(root, new_scale) { var trans = scale_centered_translation(root, new_scale); set_plot_pan_zoom(root, trans.x, trans.y, new_scale); root.selectAll(".zoomslider_thumb") .forEach(function (element, i) { var min_pos = element.data("min_pos"), max_pos = element.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(new_scale, min_scale, max_scale); element.transform(new Snap.Matrix().translate( Math.max(min_pos, Math.min( max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0)); }); }; Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var min_pos = this.data("min_pos"), max_pos = this.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("old_scale"); var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) + dx / (max_pos - min_pos); // compute the new scale var new_scale = scale_from_slider_position(xpos, min_scale, max_scale); new_scale = Math.min(max_scale, Math.max(min_scale, new_scale)); update_plot_scale(root, new_scale); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragstart = function(x, y, event) { this.animate({fill: this.data("mouseover_color")}, 100); var root = this.plotroot(); // keep track of what the scale was when we started dragging root.data("old_scale", root.data("scale")); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragend = function(event) { this.animate({fill: this.data("mouseout_color")}, 100); event.stopPropagation(); }; var toggle_color_class = function(root, color_class, ison) { var guides = root.selectAll(".guide." + color_class + ",.guide ." + color_class); var geoms = root.selectAll(".geometry." + color_class + ",.geometry ." + color_class); if (ison) { guides.animate({opacity: 0.5}, 250); geoms.animate({opacity: 0.0}, 250); } else { guides.animate({opacity: 1.0}, 250); geoms.animate({opacity: 1.0}, 250); } }; Gadfly.colorkey_swatch_click = function(event) { var root = this.plotroot(); var color_class = this.data("color_class"); if (event.shiftKey) { root.selectAll(".colorkey text") .forEach(function (element) { var other_color_class = element.data("color_class"); if (other_color_class != color_class) { toggle_color_class(root, other_color_class, element.attr("opacity") == 1.0); } }); } else { toggle_color_class(root, color_class, this.attr("opacity") == 1.0); } }; return Gadfly; })); //@ sourceURL=gadfly.js (function (glob, factory) { // AMD support if (typeof require === "function" && typeof define === "function" && define.amd) { require(["Snap.svg", "Gadfly"], function (Snap, Gadfly) { factory(Snap, Gadfly); }); } else { factory(glob.Snap, glob.Gadfly); } })(window, function (Snap, Gadfly) { var fig = Snap("#img-0ac05aad"); fig.select("#img-0ac05aad-5") .init_gadfly(); fig.select("#img-0ac05aad-7") .plotroot().data("unfocused_ygrid_color", "#D0D0E0") ; fig.select("#img-0ac05aad-7") .plotroot().data("focused_ygrid_color", "#A0A0A0") ; fig.select("#img-0ac05aad-8") .plotroot().data("unfocused_xgrid_color", "#D0D0E0") ; fig.select("#img-0ac05aad-8") .plotroot().data("focused_xgrid_color", "#A0A0A0") ; fig.select("#img-0ac05aad-12") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-0ac05aad-12") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-0ac05aad-12") .click(Gadfly.zoomslider_zoomin_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; fig.select("#img-0ac05aad-14") .data("max_pos", 120.42) ; fig.select("#img-0ac05aad-14") .data("min_pos", 103.42) ; fig.select("#img-0ac05aad-14") .click(Gadfly.zoomslider_track_click); fig.select("#img-0ac05aad-15") .data("max_pos", 120.42) ; fig.select("#img-0ac05aad-15") .data("min_pos", 103.42) ; fig.select("#img-0ac05aad-15") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-0ac05aad-15") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-0ac05aad-15") .drag(Gadfly.zoomslider_thumb_dragmove, Gadfly.zoomslider_thumb_dragstart, Gadfly.zoomslider_thumb_dragend) ; fig.select("#img-0ac05aad-16") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-0ac05aad-16") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-0ac05aad-16") .click(Gadfly.zoomslider_zoomout_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; });   In [10]: plot(x = penmbstp[:σ₁], Geom.density(), Guide.xlabel("σ₁"))   Out[10]: σ₁ -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -1.50 -1.45 -1.40 -1.35 -1.30 -1.25 -1.20 -1.15 -1.10 -1.05 -1.00 -0.95 -0.90 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85 2.90 2.95 3.00 -2 0 2 4 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 -4 -3 -2 -1 0 1 2 3 4 5 6 7 -3.0 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6.0 -3 0 3 6 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 (function(N){var k=/[\.\/]/,L=/\s*,\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if("undefined"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if("undefined"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)"zIndex"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&& (e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],"zIndex"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d["*"] ],n=2;n--;)if(d= f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if("function"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null, [a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp("(?:\\.|\\/|^)"+k+"(?:\\.|\\/|$)")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if("*"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&
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Element.prototype.plotroot = function () { var element = this; while (!element.hasClass("plotroot") && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.svgroot = function () { var element = this; while (element.node.nodeName != "svg" && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.plotbounds = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x0: bbox.x, x1: bbox.x + bbox.width, y0: bbox.y, y1: bbox.y + bbox.height }; }; Element.prototype.plotcenter = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x: bbox.x + bbox.width / 2, y: bbox.y + bbox.height / 2 }; }; // Emulate IE style mouseenter/mouseleave events, since Microsoft always // does everything right. // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/ var events = ["mouseenter", "mouseleave"]; for (i in events) { (function (event_name) { var event_name = events[i]; Element.prototype[event_name] = function (fn, scope) { if (Snap.is(fn, "function")) { var fn2 = function (event) { if (event.type != "mouseover" && event.type != "mouseout") { return; } var reltg = event.relatedTarget ? event.relatedTarget : event.type == "mouseout" ? event.toElement : event.fromElement; while (reltg && reltg != this.node) reltg = reltg.parentNode; if (reltg != this.node) { return fn.apply(this, event); } }; if (event_name == "mouseenter") { this.mouseover(fn2, scope); } else { this.mouseout(fn2, scope); } } return this; }; })(events[i]); } Element.prototype.mousewheel = function (fn, scope) { if (Snap.is(fn, "function")) { var el = this; var fn2 = function (event) { fn.apply(el, [event]); }; } this.node.addEventListener( /Firefox/i.test(navigator.userAgent) ? "DOMMouseScroll" : "mousewheel", fn2); return this; }; // Snap's attr function can be too slow for things like panning/zooming. // This is a function to directly update element attributes without going // through eve. Element.prototype.attribute = function(key, val) { if (val === undefined) { return this.node.getAttribute(key); } else { this.node.setAttribute(key, val); return this; } }; Element.prototype.init_gadfly = function() { this.mouseenter(Gadfly.plot_mouseover) .mouseleave(Gadfly.plot_mouseout) .dblclick(Gadfly.plot_dblclick) .mousewheel(Gadfly.guide_background_scroll) .drag(Gadfly.guide_background_drag_onmove, Gadfly.guide_background_drag_onstart, Gadfly.guide_background_drag_onend); this.mouseenter(function (event) { init_pan_zoom(this.plotroot()); }); return this; }; }); // When the plot is moused over, emphasize the grid lines. Gadfly.plot_mouseover = function(event) { var root = this.plotroot(); var keyboard_zoom = function(event) { if (event.which == 187) { // plus increase_zoom_by_position(root, 0.1, true); } else if (event.which == 189) { // minus increase_zoom_by_position(root, -0.1, true); } }; root.data("keyboard_zoom", keyboard_zoom); window.addEventListener("keyup", keyboard_zoom); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); xgridlines.data("unfocused_strokedash", xgridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
ygridlines.data("unfocused_strokedash",
ygridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2")); // emphasize grid lines var destcolor = root.data("focused_xgrid_color"); xgridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("focused_ygrid_color"); ygridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); // reveal zoom slider root.select(".zoomslider") .animate({opacity: 1.0}, 250); }; // Reset pan and zoom on double click Gadfly.plot_dblclick = function(event) { set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0); }; // Unemphasize grid lines on mouse out. Gadfly.plot_mouseout = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("keyboard_zoom")); root.data("keyboard_zoom", undefined); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); var destcolor = root.data("unfocused_xgrid_color"); xgridlines.attribute("stroke-dasharray", xgridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("unfocused_ygrid_color"); ygridlines.attribute("stroke-dasharray", ygridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); // hide zoom slider root.select(".zoomslider") .animate({opacity: 0.0}, 250); }; var set_geometry_transform = function(root, tx, ty, scale) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var old_scale = root.data("scale"); var xscale = xscalable ? scale : 1.0, yscale = yscalable ? scale : 1.0; tx = xscalable ? tx : 0.0; ty = yscalable ? ty : 0.0; var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale); root.selectAll(".geometry, image") .forEach(function (element, i) { element.transform(t); }); bounds = root.plotbounds(); if (yscalable) { var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale); root.selectAll(".xfixed") .forEach(function (element, i) { element.transform(xfixed_t); }); root.select(".ylabels") .transform(xfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1, 1/scale, cx, cy).add(st); element.transform(unscale_t); var y = cy * scale + ty; element.attr("visibility", bounds.y0 <= y && y <= bounds.y1 ? "visible" : "hidden"); } }); } if (xscalable) { var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0); var xtrans = new Snap.Matrix().translate(tx, 0); root.selectAll(".yfixed") .forEach(function (element, i) { element.transform(yfixed_t); }); root.select(".xlabels") .transform(yfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1/scale, 1, cx, cy).add(st); element.transform(unscale_t); var x = cx * scale + tx; element.attr("visibility", bounds.x0 <= x && x <= bounds.x1 ? "visible" : "hidden"); } }); } // we must unscale anything that is scale invariance: widths, raiduses, etc. var size_attribs = ["font-size"]; var unscaled_selection = ".geometry, .geometry *"; if (xscalable) { size_attribs.push("rx"); unscaled_selection += ", .xgridlines"; } if (yscalable) { size_attribs.push("ry"); unscaled_selection += ", .ygridlines"; } root.selectAll(unscaled_selection) .forEach(function (element, i) { // circle need special help if (element.node.nodeName == "circle") { var cx = element.attribute("cx"), cy = element.attribute("cy"); unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale, cx, cy); element.transform(unscale_t); return; } for (i in size_attribs) { var key = size_attribs[i]; var val = parseFloat(element.attribute(key)); if (val !== undefined && val != 0 && !isNaN(val)) { element.attribute(key, val * old_scale / scale); } } }); }; // Find the most appropriate tick scale and update label visibility. var update_tickscale = function(root, scale, axis) { if (!root.hasClass(axis + "scalable")) return; var tickscales = root.data(axis + "tickscales"); var best_tickscale = 1.0; var best_tickscale_dist = Infinity; for (tickscale in tickscales) { var dist = Math.abs(Math.log(tickscale) - Math.log(scale)); if (dist < best_tickscale_dist) { best_tickscale_dist = dist; best_tickscale = tickscale; } } if (best_tickscale != root.data(axis + "tickscale")) { root.data(axis + "tickscale", best_tickscale); var mark_inscale_gridlines = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; var mark_inscale_labels = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; root.select("." + axis + "gridlines").selectAll("path").forEach(mark_inscale_gridlines); root.select("." + axis + "labels").selectAll("text").forEach(mark_inscale_labels); } }; var set_plot_pan_zoom = function(root, tx, ty, scale) { var old_scale = root.data("scale"); var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; // compute the viewport derived from tx, ty, and scale var x_min = -width * scale - (scale * width - width), x_max = width * scale, y_min = -height * scale - (scale * height - height), y_max = height * scale; var x0 = bounds.x0 - scale * bounds.x0, y0 = bounds.y0 - scale * bounds.y0; var tx = Math.max(Math.min(tx - x0, x_max), x_min), ty = Math.max(Math.min(ty - y0, y_max), y_min); tx += x0; ty += y0; // when the scale change, we may need to alter which set of // ticks is being displayed if (scale != old_scale) { update_tickscale(root, scale, "x"); update_tickscale(root, scale, "y"); } set_geometry_transform(root, tx, ty, scale); root.data("scale", scale); root.data("tx", tx); root.data("ty", ty); }; var scale_centered_translation = function(root, scale) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var tx0 = root.data("tx"), ty0 = root.data("ty"); var scale0 = root.data("scale"); // how off from center the current view is var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2), yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2); // rescale offsets xoff = xoff * scale / scale0; yoff = yoff * scale / scale0; // adjust for the panel position being scaled var x_edge_adjust = bounds.x0 * (1 - scale), y_edge_adjust = bounds.y0 * (1 - scale); return { x: xoff + x_edge_adjust + (width - width * scale) / 2, y: yoff + y_edge_adjust + (height - height * scale) / 2 }; }; // Initialize data for panning zooming if it isn't already. var init_pan_zoom = function(root) { if (root.data("zoompan-ready")) { return; } // The non-scaling-stroke trick. Rather than try to correct for the // stroke-width when zooming, we force it to a fixed value. var px_per_mm = root.node.getCTM().a; // Drag events report deltas in pixels, which we'd like to convert to // millimeters. root.data("px_per_mm", px_per_mm); root.selectAll("path") .forEach(function (element, i) { sw = element.asPX("stroke-width") * px_per_mm; if (sw > 0) { element.attribute("stroke-width", sw); element.attribute("vector-effect", "non-scaling-stroke"); } }); // Store ticks labels original tranformation root.selectAll(".xlabels > text, .ylabels > text") .forEach(function (element, i) { var lm = element.transform().localMatrix; element.data("static_transform", new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f)); }); var xgridlines = root.select(".xgridlines"); var ygridlines = root.select(".ygridlines"); var xlabels = root.select(".xlabels"); var ylabels = root.select(".ylabels"); if (root.data("tx") === undefined) root.data("tx", 0); if (root.data("ty") === undefined) root.data("ty", 0); if (root.data("scale") === undefined) root.data("scale", 1.0); if (root.data("xtickscales") === undefined) { // index all the tick scales that are listed var xtickscales = {}; var ytickscales = {}; var add_x_tick_scales = function (element, i) { xtickscales[element.attribute("gadfly:scale")] = true; }; var add_y_tick_scales = function (element, i) { ytickscales[element.attribute("gadfly:scale")] = true; }; if (xgridlines) xgridlines.selectAll("path").forEach(add_x_tick_scales); if (ygridlines) ygridlines.selectAll("path").forEach(add_y_tick_scales); if (xlabels) xlabels.selectAll("text").forEach(add_x_tick_scales); if (ylabels) ylabels.selectAll("text").forEach(add_y_tick_scales); root.data("xtickscales", xtickscales); root.data("ytickscales", ytickscales); root.data("xtickscale", 1.0); } var min_scale = 1.0, max_scale = 1.0; for (scale in xtickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } for (scale in ytickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } root.data("min_scale", min_scale); root.data("max_scale", max_scale); // store the original positions of labels if (xlabels) { xlabels.selectAll("text") .forEach(function (element, i) { element.data("x", element.asPX("x")); }); } if (ylabels) { ylabels.selectAll("text") .forEach(function (element, i) { element.data("y", element.asPX("y")); }); } // mark grid lines and ticks as in or out of scale. var mark_inscale = function (element, i) { element.attribute("gadfly:inscale", element.attribute("gadfly:scale") == 1.0); }; if (xgridlines) xgridlines.selectAll("path").forEach(mark_inscale); if (ygridlines) ygridlines.selectAll("path").forEach(mark_inscale); if (xlabels) xlabels.selectAll("text").forEach(mark_inscale); if (ylabels) ylabels.selectAll("text").forEach(mark_inscale); // figure out the upper ond lower bounds on panning using the maximum // and minum grid lines var bounds = root.plotbounds(); var pan_bounds = { x0: 0.0, y0: 0.0, x1: 0.0, y1: 0.0 }; if (xgridlines) { xgridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.x1 - bbox.x < pan_bounds.x0) { pan_bounds.x0 = bounds.x1 - bbox.x; } if (bounds.x0 - bbox.x > pan_bounds.x1) { pan_bounds.x1 = bounds.x0 - bbox.x; } element.attr("visibility", "visible"); } }); } if (ygridlines) { ygridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.y1 - bbox.y < pan_bounds.y0) { pan_bounds.y0 = bounds.y1 - bbox.y; } if (bounds.y0 - bbox.y > pan_bounds.y1) { pan_bounds.y1 = bounds.y0 - bbox.y; } element.attr("visibility", "visible"); } }); } // nudge these values a little pan_bounds.x0 -= 5; pan_bounds.x1 += 5; pan_bounds.y0 -= 5; pan_bounds.y1 += 5; root.data("pan_bounds", pan_bounds); root.data("zoompan-ready", true) }; // drag actions, i.e. zooming and panning var pan_action = { start: function(root, x, y, event) { root.data("dx", 0); root.data("dy", 0); root.data("tx0", root.data("tx")); root.data("ty0", root.data("ty")); }, update: function(root, dx, dy, x, y, event) { var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var tx0 = root.data("tx"), ty0 = root.data("ty"); var dx0 = root.data("dx"), dy0 = root.data("dy"); root.data("dx", dx); root.data("dy", dy); dx = dx - dx0; dy = dy - dy0; var tx = tx0 + dx, ty = ty0 + dy; set_plot_pan_zoom(root, tx, ty, root.data("scale")); }, end: function(root, event) { }, cancel: function(root) { set_plot_pan_zoom(root, root.data("tx0"), root.data("ty0"), root.data("scale")); } }; var zoom_box; var zoom_action = { start: function(root, x, y, event) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var ratio = width / height; var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); x = xscalable ? x / px_per_mm : bounds.x0; y = yscalable ? y / px_per_mm : bounds.y0; var w = xscalable ? 0 : width; var h = yscalable ? 0 : height; zoom_box = root.rect(x, y, w, h).attr({ "fill": "#000", "opacity": 0.25 }); zoom_box.data("ratio", ratio); }, update: function(root, dx, dy, x, y, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); var bounds = root.plotbounds(); if (yscalable) { y /= px_per_mm; y = Math.max(bounds.y0, y); y = Math.min(bounds.y1, y); } else { y = bounds.y1; } if (xscalable) { x /= px_per_mm; x = Math.max(bounds.x0, x); x = Math.min(bounds.x1, x); } else { x = bounds.x1; } dx = x - zoom_box.attr("x"); dy = y - zoom_box.attr("y"); if (xscalable && yscalable) { var ratio = zoom_box.data("ratio"); var width = Math.min(Math.abs(dx), ratio * Math.abs(dy)); var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio); dx = width * dx / Math.abs(dx); dy = height * dy / Math.abs(dy); } var xoffset = 0, yoffset = 0; if (dx < 0) { xoffset = dx; dx = -1 * dx; } if (dy < 0) { yoffset = dy; dy = -1 * dy; } if (isNaN(dy)) { dy = 0.0; } if (isNaN(dx)) { dx = 0.0; } zoom_box.transform("T" + xoffset + "," + yoffset); zoom_box.attr("width", dx); zoom_box.attr("height", dy); }, end: function(root, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var zoom_bounds = zoom_box.getBBox(); if (zoom_bounds.width * zoom_bounds.height <= 0) { return; } var plot_bounds = root.plotbounds(); var zoom_factor = 1.0; if (yscalable) { zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height; } else { zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width; } var tx = (root.data("tx") - zoom_bounds.x) * zoom_factor + plot_bounds.x0, ty = (root.data("ty") - zoom_bounds.y) * zoom_factor + plot_bounds.y0; set_plot_pan_zoom(root, tx, ty, root.data("scale") * zoom_factor); zoom_box.remove(); }, cancel: function(root) { zoom_box.remove(); } }; Gadfly.guide_background_drag_onstart = function(x, y, event) { var root = this.plotroot(); var scalable = root.hasClass("xscalable") || root.hasClass("yscalable"); var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable; var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable; var drag_action = zoomable ? zoom_action : panable ? pan_action : undefined; root.data("drag_action", drag_action); if (drag_action) { var cancel_drag_action = function(event) { if (event.which == 27) { // esc key drag_action.cancel(root); root.data("drag_action", undefined); } }; window.addEventListener("keyup", cancel_drag_action); root.data("cancel_drag_action", cancel_drag_action); drag_action.start(root, x, y, event); } }; Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.update(root, dx, dy, x, y, event); } }; Gadfly.guide_background_drag_onend = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("cancel_drag_action")); root.data("cancel_drag_action", undefined); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.end(root, event); } root.data("drag_action", undefined); }; Gadfly.guide_background_scroll = function(event) { if (event.shiftKey) { increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta); event.preventDefault(); } }; Gadfly.zoomslider_button_mouseover = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseover_color")}, 100); }; Gadfly.zoomslider_button_mouseout = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseout_color")}, 100); }; Gadfly.zoomslider_zoomout_click = function(event) { increase_zoom_by_position(this.plotroot(), -0.1, true); }; Gadfly.zoomslider_zoomin_click = function(event) { increase_zoom_by_position(this.plotroot(), 0.1, true); }; Gadfly.zoomslider_track_click = function(event) { // TODO }; // Map slider position x to scale y using the function y = a*exp(b*x)+c. // The constants a, b, and c are solved using the constraint that the function // should go through the points (0; min_scale), (0.5; 1), and (1; max_scale). var scale_from_slider_position = function(position, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return a * Math.exp(b * position) + c; } // inverse of scale_from_slider_position var slider_position_from_scale = function(scale, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return 1 / b * Math.log((scale - c) / a); } var increase_zoom_by_position = function(root, delta_position, animate) { var scale = root.data("scale"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var position = slider_position_from_scale(scale, min_scale, max_scale); position += delta_position; scale = scale_from_slider_position(position, min_scale, max_scale); set_zoom(root, scale, animate); } var set_zoom = function(root, scale, animate) { var min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("scale"); var new_scale = Math.max(min_scale, Math.min(scale, max_scale)); if (animate) { Snap.animate( old_scale, new_scale, function (new_scale) { update_plot_scale(root, new_scale); }, 200); } else { update_plot_scale(root, new_scale); } } var update_plot_scale = function(root, new_scale) { var trans = scale_centered_translation(root, new_scale); set_plot_pan_zoom(root, trans.x, trans.y, new_scale); root.selectAll(".zoomslider_thumb") .forEach(function (element, i) { var min_pos = element.data("min_pos"), max_pos = element.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(new_scale, min_scale, max_scale); element.transform(new Snap.Matrix().translate( Math.max(min_pos, Math.min( max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0)); }); }; Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var min_pos = this.data("min_pos"), max_pos = this.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("old_scale"); var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) + dx / (max_pos - min_pos); // compute the new scale var new_scale = scale_from_slider_position(xpos, min_scale, max_scale); new_scale = Math.min(max_scale, Math.max(min_scale, new_scale)); update_plot_scale(root, new_scale); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragstart = function(x, y, event) { this.animate({fill: this.data("mouseover_color")}, 100); var root = this.plotroot(); // keep track of what the scale was when we started dragging root.data("old_scale", root.data("scale")); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragend = function(event) { this.animate({fill: this.data("mouseout_color")}, 100); event.stopPropagation(); }; var toggle_color_class = function(root, color_class, ison) { var guides = root.selectAll(".guide." + color_class + ",.guide ." + color_class); var geoms = root.selectAll(".geometry." + color_class + ",.geometry ." + color_class); if (ison) { guides.animate({opacity: 0.5}, 250); geoms.animate({opacity: 0.0}, 250); } else { guides.animate({opacity: 1.0}, 250); geoms.animate({opacity: 1.0}, 250); } }; Gadfly.colorkey_swatch_click = function(event) { var root = this.plotroot(); var color_class = this.data("color_class"); if (event.shiftKey) { root.selectAll(".colorkey text") .forEach(function (element) { var other_color_class = element.data("color_class"); if (other_color_class != color_class) { toggle_color_class(root, other_color_class, element.attr("opacity") == 1.0); } }); } else { toggle_color_class(root, color_class, this.attr("opacity") == 1.0); } }; return Gadfly; })); //@ sourceURL=gadfly.js (function (glob, factory) { // AMD support if (typeof require === "function" && typeof define === "function" && define.amd) { require(["Snap.svg", "Gadfly"], function (Snap, Gadfly) { factory(Snap, Gadfly); }); } else { factory(glob.Snap, glob.Gadfly); } })(window, function (Snap, Gadfly) { var fig = Snap("#img-eecd75c7"); fig.select("#img-eecd75c7-5") .init_gadfly(); fig.select("#img-eecd75c7-7") .plotroot().data("unfocused_ygrid_color", "#D0D0E0") ; fig.select("#img-eecd75c7-7") .plotroot().data("focused_ygrid_color", "#A0A0A0") ; fig.select("#img-eecd75c7-8") .plotroot().data("unfocused_xgrid_color", "#D0D0E0") ; fig.select("#img-eecd75c7-8") .plotroot().data("focused_xgrid_color", "#A0A0A0") ; fig.select("#img-eecd75c7-12") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-eecd75c7-12") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-eecd75c7-12") .click(Gadfly.zoomslider_zoomin_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; fig.select("#img-eecd75c7-14") .data("max_pos", 120.42) ; fig.select("#img-eecd75c7-14") .data("min_pos", 103.42) ; fig.select("#img-eecd75c7-14") .click(Gadfly.zoomslider_track_click); fig.select("#img-eecd75c7-15") .data("max_pos", 120.42) ; fig.select("#img-eecd75c7-15") .data("min_pos", 103.42) ; fig.select("#img-eecd75c7-15") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-eecd75c7-15") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-eecd75c7-15") .drag(Gadfly.zoomslider_thumb_dragmove, Gadfly.zoomslider_thumb_dragstart, Gadfly.zoomslider_thumb_dragend) ; fig.select("#img-eecd75c7-16") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-eecd75c7-16") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-eecd75c7-16") .click(Gadfly.zoomslider_zoomout_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; });   In [11]: plot(x = penmbstp[:σ₂], Geom.density(), Guide.xlabel("σ₂"))   Out[11]: σ₂ -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 -7.0 -6.8 -6.6 -6.4 -6.2 -6.0 -5.8 -5.6 -5.4 -5.2 -5.0 -4.8 -4.6 -4.4 -4.2 -4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6 10.8 11.0 -10 0 10 20 -7.0 -6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 -1 0 1 2 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 (function(N){var k=/[\.\/]/,L=/\s*,\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if("undefined"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if("undefined"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)"zIndex"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&& (e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],"zIndex"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d["*"] ],n=2;n--;)if(d= f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if("function"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null, [a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp("(?:\\.|\\/|^)"+k+"(?:\\.|\\/|$)")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if("*"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&
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Element.prototype.plotroot = function () { var element = this; while (!element.hasClass("plotroot") && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.svgroot = function () { var element = this; while (element.node.nodeName != "svg" && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.plotbounds = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x0: bbox.x, x1: bbox.x + bbox.width, y0: bbox.y, y1: bbox.y + bbox.height }; }; Element.prototype.plotcenter = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x: bbox.x + bbox.width / 2, y: bbox.y + bbox.height / 2 }; }; // Emulate IE style mouseenter/mouseleave events, since Microsoft always // does everything right. // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/ var events = ["mouseenter", "mouseleave"]; for (i in events) { (function (event_name) { var event_name = events[i]; Element.prototype[event_name] = function (fn, scope) { if (Snap.is(fn, "function")) { var fn2 = function (event) { if (event.type != "mouseover" && event.type != "mouseout") { return; } var reltg = event.relatedTarget ? event.relatedTarget : event.type == "mouseout" ? event.toElement : event.fromElement; while (reltg && reltg != this.node) reltg = reltg.parentNode; if (reltg != this.node) { return fn.apply(this, event); } }; if (event_name == "mouseenter") { this.mouseover(fn2, scope); } else { this.mouseout(fn2, scope); } } return this; }; })(events[i]); } Element.prototype.mousewheel = function (fn, scope) { if (Snap.is(fn, "function")) { var el = this; var fn2 = function (event) { fn.apply(el, [event]); }; } this.node.addEventListener( /Firefox/i.test(navigator.userAgent) ? "DOMMouseScroll" : "mousewheel", fn2); return this; }; // Snap's attr function can be too slow for things like panning/zooming. // This is a function to directly update element attributes without going // through eve. Element.prototype.attribute = function(key, val) { if (val === undefined) { return this.node.getAttribute(key); } else { this.node.setAttribute(key, val); return this; } }; Element.prototype.init_gadfly = function() { this.mouseenter(Gadfly.plot_mouseover) .mouseleave(Gadfly.plot_mouseout) .dblclick(Gadfly.plot_dblclick) .mousewheel(Gadfly.guide_background_scroll) .drag(Gadfly.guide_background_drag_onmove, Gadfly.guide_background_drag_onstart, Gadfly.guide_background_drag_onend); this.mouseenter(function (event) { init_pan_zoom(this.plotroot()); }); return this; }; }); // When the plot is moused over, emphasize the grid lines. Gadfly.plot_mouseover = function(event) { var root = this.plotroot(); var keyboard_zoom = function(event) { if (event.which == 187) { // plus increase_zoom_by_position(root, 0.1, true); } else if (event.which == 189) { // minus increase_zoom_by_position(root, -0.1, true); } }; root.data("keyboard_zoom", keyboard_zoom); window.addEventListener("keyup", keyboard_zoom); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); xgridlines.data("unfocused_strokedash", xgridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
ygridlines.data("unfocused_strokedash",
ygridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2")); // emphasize grid lines var destcolor = root.data("focused_xgrid_color"); xgridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("focused_ygrid_color"); ygridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); // reveal zoom slider root.select(".zoomslider") .animate({opacity: 1.0}, 250); }; // Reset pan and zoom on double click Gadfly.plot_dblclick = function(event) { set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0); }; // Unemphasize grid lines on mouse out. Gadfly.plot_mouseout = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("keyboard_zoom")); root.data("keyboard_zoom", undefined); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); var destcolor = root.data("unfocused_xgrid_color"); xgridlines.attribute("stroke-dasharray", xgridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("unfocused_ygrid_color"); ygridlines.attribute("stroke-dasharray", ygridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); // hide zoom slider root.select(".zoomslider") .animate({opacity: 0.0}, 250); }; var set_geometry_transform = function(root, tx, ty, scale) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var old_scale = root.data("scale"); var xscale = xscalable ? scale : 1.0, yscale = yscalable ? scale : 1.0; tx = xscalable ? tx : 0.0; ty = yscalable ? ty : 0.0; var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale); root.selectAll(".geometry, image") .forEach(function (element, i) { element.transform(t); }); bounds = root.plotbounds(); if (yscalable) { var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale); root.selectAll(".xfixed") .forEach(function (element, i) { element.transform(xfixed_t); }); root.select(".ylabels") .transform(xfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1, 1/scale, cx, cy).add(st); element.transform(unscale_t); var y = cy * scale + ty; element.attr("visibility", bounds.y0 <= y && y <= bounds.y1 ? "visible" : "hidden"); } }); } if (xscalable) { var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0); var xtrans = new Snap.Matrix().translate(tx, 0); root.selectAll(".yfixed") .forEach(function (element, i) { element.transform(yfixed_t); }); root.select(".xlabels") .transform(yfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1/scale, 1, cx, cy).add(st); element.transform(unscale_t); var x = cx * scale + tx; element.attr("visibility", bounds.x0 <= x && x <= bounds.x1 ? "visible" : "hidden"); } }); } // we must unscale anything that is scale invariance: widths, raiduses, etc. var size_attribs = ["font-size"]; var unscaled_selection = ".geometry, .geometry *"; if (xscalable) { size_attribs.push("rx"); unscaled_selection += ", .xgridlines"; } if (yscalable) { size_attribs.push("ry"); unscaled_selection += ", .ygridlines"; } root.selectAll(unscaled_selection) .forEach(function (element, i) { // circle need special help if (element.node.nodeName == "circle") { var cx = element.attribute("cx"), cy = element.attribute("cy"); unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale, cx, cy); element.transform(unscale_t); return; } for (i in size_attribs) { var key = size_attribs[i]; var val = parseFloat(element.attribute(key)); if (val !== undefined && val != 0 && !isNaN(val)) { element.attribute(key, val * old_scale / scale); } } }); }; // Find the most appropriate tick scale and update label visibility. var update_tickscale = function(root, scale, axis) { if (!root.hasClass(axis + "scalable")) return; var tickscales = root.data(axis + "tickscales"); var best_tickscale = 1.0; var best_tickscale_dist = Infinity; for (tickscale in tickscales) { var dist = Math.abs(Math.log(tickscale) - Math.log(scale)); if (dist < best_tickscale_dist) { best_tickscale_dist = dist; best_tickscale = tickscale; } } if (best_tickscale != root.data(axis + "tickscale")) { root.data(axis + "tickscale", best_tickscale); var mark_inscale_gridlines = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; var mark_inscale_labels = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; root.select("." + axis + "gridlines").selectAll("path").forEach(mark_inscale_gridlines); root.select("." + axis + "labels").selectAll("text").forEach(mark_inscale_labels); } }; var set_plot_pan_zoom = function(root, tx, ty, scale) { var old_scale = root.data("scale"); var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; // compute the viewport derived from tx, ty, and scale var x_min = -width * scale - (scale * width - width), x_max = width * scale, y_min = -height * scale - (scale * height - height), y_max = height * scale; var x0 = bounds.x0 - scale * bounds.x0, y0 = bounds.y0 - scale * bounds.y0; var tx = Math.max(Math.min(tx - x0, x_max), x_min), ty = Math.max(Math.min(ty - y0, y_max), y_min); tx += x0; ty += y0; // when the scale change, we may need to alter which set of // ticks is being displayed if (scale != old_scale) { update_tickscale(root, scale, "x"); update_tickscale(root, scale, "y"); } set_geometry_transform(root, tx, ty, scale); root.data("scale", scale); root.data("tx", tx); root.data("ty", ty); }; var scale_centered_translation = function(root, scale) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var tx0 = root.data("tx"), ty0 = root.data("ty"); var scale0 = root.data("scale"); // how off from center the current view is var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2), yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2); // rescale offsets xoff = xoff * scale / scale0; yoff = yoff * scale / scale0; // adjust for the panel position being scaled var x_edge_adjust = bounds.x0 * (1 - scale), y_edge_adjust = bounds.y0 * (1 - scale); return { x: xoff + x_edge_adjust + (width - width * scale) / 2, y: yoff + y_edge_adjust + (height - height * scale) / 2 }; }; // Initialize data for panning zooming if it isn't already. var init_pan_zoom = function(root) { if (root.data("zoompan-ready")) { return; } // The non-scaling-stroke trick. Rather than try to correct for the // stroke-width when zooming, we force it to a fixed value. var px_per_mm = root.node.getCTM().a; // Drag events report deltas in pixels, which we'd like to convert to // millimeters. root.data("px_per_mm", px_per_mm); root.selectAll("path") .forEach(function (element, i) { sw = element.asPX("stroke-width") * px_per_mm; if (sw > 0) { element.attribute("stroke-width", sw); element.attribute("vector-effect", "non-scaling-stroke"); } }); // Store ticks labels original tranformation root.selectAll(".xlabels > text, .ylabels > text") .forEach(function (element, i) { var lm = element.transform().localMatrix; element.data("static_transform", new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f)); }); var xgridlines = root.select(".xgridlines"); var ygridlines = root.select(".ygridlines"); var xlabels = root.select(".xlabels"); var ylabels = root.select(".ylabels"); if (root.data("tx") === undefined) root.data("tx", 0); if (root.data("ty") === undefined) root.data("ty", 0); if (root.data("scale") === undefined) root.data("scale", 1.0); if (root.data("xtickscales") === undefined) { // index all the tick scales that are listed var xtickscales = {}; var ytickscales = {}; var add_x_tick_scales = function (element, i) { xtickscales[element.attribute("gadfly:scale")] = true; }; var add_y_tick_scales = function (element, i) { ytickscales[element.attribute("gadfly:scale")] = true; }; if (xgridlines) xgridlines.selectAll("path").forEach(add_x_tick_scales); if (ygridlines) ygridlines.selectAll("path").forEach(add_y_tick_scales); if (xlabels) xlabels.selectAll("text").forEach(add_x_tick_scales); if (ylabels) ylabels.selectAll("text").forEach(add_y_tick_scales); root.data("xtickscales", xtickscales); root.data("ytickscales", ytickscales); root.data("xtickscale", 1.0); } var min_scale = 1.0, max_scale = 1.0; for (scale in xtickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } for (scale in ytickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } root.data("min_scale", min_scale); root.data("max_scale", max_scale); // store the original positions of labels if (xlabels) { xlabels.selectAll("text") .forEach(function (element, i) { element.data("x", element.asPX("x")); }); } if (ylabels) { ylabels.selectAll("text") .forEach(function (element, i) { element.data("y", element.asPX("y")); }); } // mark grid lines and ticks as in or out of scale. var mark_inscale = function (element, i) { element.attribute("gadfly:inscale", element.attribute("gadfly:scale") == 1.0); }; if (xgridlines) xgridlines.selectAll("path").forEach(mark_inscale); if (ygridlines) ygridlines.selectAll("path").forEach(mark_inscale); if (xlabels) xlabels.selectAll("text").forEach(mark_inscale); if (ylabels) ylabels.selectAll("text").forEach(mark_inscale); // figure out the upper ond lower bounds on panning using the maximum // and minum grid lines var bounds = root.plotbounds(); var pan_bounds = { x0: 0.0, y0: 0.0, x1: 0.0, y1: 0.0 }; if (xgridlines) { xgridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.x1 - bbox.x < pan_bounds.x0) { pan_bounds.x0 = bounds.x1 - bbox.x; } if (bounds.x0 - bbox.x > pan_bounds.x1) { pan_bounds.x1 = bounds.x0 - bbox.x; } element.attr("visibility", "visible"); } }); } if (ygridlines) { ygridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.y1 - bbox.y < pan_bounds.y0) { pan_bounds.y0 = bounds.y1 - bbox.y; } if (bounds.y0 - bbox.y > pan_bounds.y1) { pan_bounds.y1 = bounds.y0 - bbox.y; } element.attr("visibility", "visible"); } }); } // nudge these values a little pan_bounds.x0 -= 5; pan_bounds.x1 += 5; pan_bounds.y0 -= 5; pan_bounds.y1 += 5; root.data("pan_bounds", pan_bounds); root.data("zoompan-ready", true) }; // drag actions, i.e. zooming and panning var pan_action = { start: function(root, x, y, event) { root.data("dx", 0); root.data("dy", 0); root.data("tx0", root.data("tx")); root.data("ty0", root.data("ty")); }, update: function(root, dx, dy, x, y, event) { var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var tx0 = root.data("tx"), ty0 = root.data("ty"); var dx0 = root.data("dx"), dy0 = root.data("dy"); root.data("dx", dx); root.data("dy", dy); dx = dx - dx0; dy = dy - dy0; var tx = tx0 + dx, ty = ty0 + dy; set_plot_pan_zoom(root, tx, ty, root.data("scale")); }, end: function(root, event) { }, cancel: function(root) { set_plot_pan_zoom(root, root.data("tx0"), root.data("ty0"), root.data("scale")); } }; var zoom_box; var zoom_action = { start: function(root, x, y, event) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var ratio = width / height; var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); x = xscalable ? x / px_per_mm : bounds.x0; y = yscalable ? y / px_per_mm : bounds.y0; var w = xscalable ? 0 : width; var h = yscalable ? 0 : height; zoom_box = root.rect(x, y, w, h).attr({ "fill": "#000", "opacity": 0.25 }); zoom_box.data("ratio", ratio); }, update: function(root, dx, dy, x, y, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); var bounds = root.plotbounds(); if (yscalable) { y /= px_per_mm; y = Math.max(bounds.y0, y); y = Math.min(bounds.y1, y); } else { y = bounds.y1; } if (xscalable) { x /= px_per_mm; x = Math.max(bounds.x0, x); x = Math.min(bounds.x1, x); } else { x = bounds.x1; } dx = x - zoom_box.attr("x"); dy = y - zoom_box.attr("y"); if (xscalable && yscalable) { var ratio = zoom_box.data("ratio"); var width = Math.min(Math.abs(dx), ratio * Math.abs(dy)); var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio); dx = width * dx / Math.abs(dx); dy = height * dy / Math.abs(dy); } var xoffset = 0, yoffset = 0; if (dx < 0) { xoffset = dx; dx = -1 * dx; } if (dy < 0) { yoffset = dy; dy = -1 * dy; } if (isNaN(dy)) { dy = 0.0; } if (isNaN(dx)) { dx = 0.0; } zoom_box.transform("T" + xoffset + "," + yoffset); zoom_box.attr("width", dx); zoom_box.attr("height", dy); }, end: function(root, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var zoom_bounds = zoom_box.getBBox(); if (zoom_bounds.width * zoom_bounds.height <= 0) { return; } var plot_bounds = root.plotbounds(); var zoom_factor = 1.0; if (yscalable) { zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height; } else { zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width; } var tx = (root.data("tx") - zoom_bounds.x) * zoom_factor + plot_bounds.x0, ty = (root.data("ty") - zoom_bounds.y) * zoom_factor + plot_bounds.y0; set_plot_pan_zoom(root, tx, ty, root.data("scale") * zoom_factor); zoom_box.remove(); }, cancel: function(root) { zoom_box.remove(); } }; Gadfly.guide_background_drag_onstart = function(x, y, event) { var root = this.plotroot(); var scalable = root.hasClass("xscalable") || root.hasClass("yscalable"); var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable; var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable; var drag_action = zoomable ? zoom_action : panable ? pan_action : undefined; root.data("drag_action", drag_action); if (drag_action) { var cancel_drag_action = function(event) { if (event.which == 27) { // esc key drag_action.cancel(root); root.data("drag_action", undefined); } }; window.addEventListener("keyup", cancel_drag_action); root.data("cancel_drag_action", cancel_drag_action); drag_action.start(root, x, y, event); } }; Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.update(root, dx, dy, x, y, event); } }; Gadfly.guide_background_drag_onend = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("cancel_drag_action")); root.data("cancel_drag_action", undefined); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.end(root, event); } root.data("drag_action", undefined); }; Gadfly.guide_background_scroll = function(event) { if (event.shiftKey) { increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta); event.preventDefault(); } }; Gadfly.zoomslider_button_mouseover = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseover_color")}, 100); }; Gadfly.zoomslider_button_mouseout = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseout_color")}, 100); }; Gadfly.zoomslider_zoomout_click = function(event) { increase_zoom_by_position(this.plotroot(), -0.1, true); }; Gadfly.zoomslider_zoomin_click = function(event) { increase_zoom_by_position(this.plotroot(), 0.1, true); }; Gadfly.zoomslider_track_click = function(event) { // TODO }; // Map slider position x to scale y using the function y = a*exp(b*x)+c. // The constants a, b, and c are solved using the constraint that the function // should go through the points (0; min_scale), (0.5; 1), and (1; max_scale). var scale_from_slider_position = function(position, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return a * Math.exp(b * position) + c; } // inverse of scale_from_slider_position var slider_position_from_scale = function(scale, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return 1 / b * Math.log((scale - c) / a); } var increase_zoom_by_position = function(root, delta_position, animate) { var scale = root.data("scale"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var position = slider_position_from_scale(scale, min_scale, max_scale); position += delta_position; scale = scale_from_slider_position(position, min_scale, max_scale); set_zoom(root, scale, animate); } var set_zoom = function(root, scale, animate) { var min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("scale"); var new_scale = Math.max(min_scale, Math.min(scale, max_scale)); if (animate) { Snap.animate( old_scale, new_scale, function (new_scale) { update_plot_scale(root, new_scale); }, 200); } else { update_plot_scale(root, new_scale); } } var update_plot_scale = function(root, new_scale) { var trans = scale_centered_translation(root, new_scale); set_plot_pan_zoom(root, trans.x, trans.y, new_scale); root.selectAll(".zoomslider_thumb") .forEach(function (element, i) { var min_pos = element.data("min_pos"), max_pos = element.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(new_scale, min_scale, max_scale); element.transform(new Snap.Matrix().translate( Math.max(min_pos, Math.min( max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0)); }); }; Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var min_pos = this.data("min_pos"), max_pos = this.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("old_scale"); var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) + dx / (max_pos - min_pos); // compute the new scale var new_scale = scale_from_slider_position(xpos, min_scale, max_scale); new_scale = Math.min(max_scale, Math.max(min_scale, new_scale)); update_plot_scale(root, new_scale); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragstart = function(x, y, event) { this.animate({fill: this.data("mouseover_color")}, 100); var root = this.plotroot(); // keep track of what the scale was when we started dragging root.data("old_scale", root.data("scale")); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragend = function(event) { this.animate({fill: this.data("mouseout_color")}, 100); event.stopPropagation(); }; var toggle_color_class = function(root, color_class, ison) { var guides = root.selectAll(".guide." + color_class + ",.guide ." + color_class); var geoms = root.selectAll(".geometry." + color_class + ",.geometry ." + color_class); if (ison) { guides.animate({opacity: 0.5}, 250); geoms.animate({opacity: 0.0}, 250); } else { guides.animate({opacity: 1.0}, 250); geoms.animate({opacity: 1.0}, 250); } }; Gadfly.colorkey_swatch_click = function(event) { var root = this.plotroot(); var color_class = this.data("color_class"); if (event.shiftKey) { root.selectAll(".colorkey text") .forEach(function (element) { var other_color_class = element.data("color_class"); if (other_color_class != color_class) { toggle_color_class(root, other_color_class, element.attr("opacity") == 1.0); } }); } else { toggle_color_class(root, color_class, this.attr("opacity") == 1.0); } }; return Gadfly; })); //@ sourceURL=gadfly.js (function (glob, factory) { // AMD support if (typeof require === "function" && typeof define === "function" && define.amd) { require(["Snap.svg", "Gadfly"], function (Snap, Gadfly) { factory(Snap, Gadfly); }); } else { factory(glob.Snap, glob.Gadfly); } })(window, function (Snap, Gadfly) { var fig = Snap("#img-a9642b17"); fig.select("#img-a9642b17-5") .init_gadfly(); fig.select("#img-a9642b17-7") .plotroot().data("unfocused_ygrid_color", "#D0D0E0") ; fig.select("#img-a9642b17-7") .plotroot().data("focused_ygrid_color", "#A0A0A0") ; fig.select("#img-a9642b17-8") .plotroot().data("unfocused_xgrid_color", "#D0D0E0") ; fig.select("#img-a9642b17-8") .plotroot().data("focused_xgrid_color", "#A0A0A0") ; fig.select("#img-a9642b17-12") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-a9642b17-12") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-a9642b17-12") .click(Gadfly.zoomslider_zoomin_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; fig.select("#img-a9642b17-14") .data("max_pos", 120.42) ; fig.select("#img-a9642b17-14") .data("min_pos", 103.42) ; fig.select("#img-a9642b17-14") .click(Gadfly.zoomslider_track_click); fig.select("#img-a9642b17-15") .data("max_pos", 120.42) ; fig.select("#img-a9642b17-15") .data("min_pos", 103.42) ; fig.select("#img-a9642b17-15") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-a9642b17-15") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-a9642b17-15") .drag(Gadfly.zoomslider_thumb_dragmove, Gadfly.zoomslider_thumb_dragstart, Gadfly.zoomslider_thumb_dragend) ; fig.select("#img-a9642b17-16") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-a9642b17-16") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-a9642b17-16") .click(Gadfly.zoomslider_zoomout_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; });  A profile zeta plot (Fig. [fig:fm03prplot]) for the parameters in model [fig:fm03prplot] leads to conclusions similar to those from Fig. [fig:fm1prof] for model in the previous chapter. The fixed-effect parameter,$\beta_1$, for the constant term has symmetric intervals and is over-dispersed relative to the normal distribution. The logarithm of$\sigma$has a good normal approximation but the standard deviations of the random effects,$\sigma_1$and$\sigma_2$, are skewed. The skewness for$\sigma_2$is worse than that for$\sigma_1$, because the estimate of$\sigma_2$is less precise than that of$\sigma_1$, in both absolute and relative senses. For an absolute comparison we compare the widths of the confidence intervals for these parameters.  2.5 % 97.5 % .sig01 0.6335658 1.1821040 .sig02 1.0957893 3.5562919 .sigma 0.4858454 0.6294535 (Intercept) 21.2666274 24.6778176 In a relative comparison we examine the ratio of the endpoints of the interval divided by the estimate.  2.5 % 97.5 % .sig01 0.7492746 1.397993 .sig02 0.6188634 2.008469 The lack of precision in the estimate of$\sigma_2$is a consequence of only having 6 distinct levels of the factor. The factor, on the other hand, has 24 distinct levels. In general it is more difficult to estimate a measure of spread, such as the standard deviation, than to estimate a measure of location, such as a mean, especially when the number of levels of the factor is small. Six levels are about the minimum number required for obtaining sensible estimates of standard deviations for simple, scalar random effects terms. The profile pairs plot (Fig. [fig:fm03prpairs]) [fig:fm03prpairs] shows patterns similar to those in Fig. [fig:fm1profpair] for pairs of parameters in model fit to the data. On the$\zeta$scale (panels below the diagonal) the profile traces are nearly straight and orthogonal with the exception of the trace of$\zeta(\sigma_2)$on$\zeta(\beta_0)$(the horizontal trace for the panel in the$(4,2)$position). The pattern of this trace is similar to the pattern of the trace of$\zeta(\sigma_1)$on$\zeta(\beta_1)$in Fig. [fig:fm1profpair]. Moving$\beta_0$from its estimate,$\widehat{\beta}_0$, in either direction will increase the residual sum of squares. The increase in the residual variability is reflected in an increase of one or more of the dispersion parameters. The balanced experimental design results in a fixed estimate of$\sigma$and the extra apparent variability must be incorporated into$\sigma_1$or$\sigma_2$. Contours in panels of parameter pairs on the original scales (i.e. panels above the diagonal) can show considerable distortion from the ideal elliptical shape. For example, contours in the$\sigma_2$versus$\sigma_1$panel (the$(1,2)$position) and the$\log(\sigma)$versus$\sigma_2$panel (in the$(2,3)$position) are dramatically non-elliptical. However, the distortion of the contours is not due to these parameter estimates depending strongly on each other. It is almost entirely due to the choice of scale for$\sigma_1$and$\sigma_2$. When we plot the contours on the scale of$\log(\sigma_1)$and$\log(\sigma_2)$instead (Fig. [fig:lpr03pairs]) [fig:fm03lprpairs] they are much closer to the elliptical pattern. Conversely, if we tried to plot contours on the scale of$\sigma_1^2$and$\sigma_2^2$(not shown), they would be hideously distorted. ## A Model With Nested Random Effects In this section we again consider a simple example, this time fitting a model with nested grouping factors for the random effects. ### The Pastes Data The third example from Davies (1972) is described as coming from deliveries of a chemical paste product contained in casks where, in addition to sampling and testing errors, there are variations in quality between deliveries …As a routine, three casks selected at random from each delivery were sampled and the samples were kept for reference. …Ten of the delivery batches were sampled at random and two analytical tests carried out on each of the 30 samples. The structure and summary of the data object are  In [12]: pastes = Feather.read(joinpath(datadir, "Pastes.feather"), nullable = false); names(pastes)   Out[12]: 4-element Array{Symbol,1}: :strength :batch :cask :sample  As stated in the description in Davies (1972), there are 30 samples, three from each of the 10 delivery batches. We have labelled the levels of the sample factor with the label of the batch factor followed by a, b or c to distinguish the three samples taken from that batch.  In [13]: freqtable(convert(Vector{String}, pastes[:batch]), convert(Vector{String}, pastes[:sample]))   Out[13]: 10×30 Named Array{Int64,2} Dim1 ╲ Dim2 │ A:a A:b A:c B:a B:b B:c … I:a I:b I:c J:a J:b J:c ────────────┼────────────────────────────────────────────────────────────── A │ 2 2 2 0 0 0 … 0 0 0 0 0 0 B │ 0 0 0 2 2 2 0 0 0 0 0 0 C │ 0 0 0 0 0 0 0 0 0 0 0 0 D │ 0 0 0 0 0 0 0 0 0 0 0 0 E │ 0 0 0 0 0 0 0 0 0 0 0 0 F │ 0 0 0 0 0 0 0 0 0 0 0 0 G │ 0 0 0 0 0 0 0 0 0 0 0 0 H │ 0 0 0 0 0 0 0 0 0 0 0 0 I │ 0 0 0 0 0 0 2 2 2 0 0 0 J │ 0 0 0 0 0 0 … 0 0 0 2 2 2  When plotting the strength versus sample and in the data we should remember that we have two strength measurements on each of the 30 samples. It is tempting to use the cask designation (‘a’, ‘b’ and ‘c’) to determine, say, the plotting symbol within a sample. It would be fine to do this within a batch but the plot would be misleading if we used the same symbol for cask ‘a’ in different batches. There is no relationship between cask ‘a’ in batch ‘A’ and cask ‘a’ in batch ‘B’. The labels ‘a’, ‘b’ and ‘c’ are used only to distinguish the three samples within a batch; they do not have a meaning across batches. [fig:Pastesplot] In Fig. [fig:Pastesplot] we plot the two strength measurements on each of the samples within each of the batches and join up the average strength for each sample. The perceptive reader will have noticed that the levels of the factors on the vertical axis in this figure, and in Fig. [fig:Dyestuffdot] and [fig:Penicillindot], have been reordered according to increasing average response. In all these cases there is no inherent ordering of the levels of the covariate such as or . Rather than confuse our interpretation of the plot by determining the vertical displacement of points according to a random ordering, we impose an ordering according to increasing mean response. This allows us to more easily check for structure in the data, including undesirable characteristics like increasing variability of the response with increasing mean level of the response. In Fig. [fig:Pastesplot] we order the samples within each batch separately then order the batches according to increasing mean strength. Figure [fig:Pastesplot] shows considerable variability in strength between samples relative to the variability within samples. There is some indication of variability between batches, in addition to the variability induced by the samples, but not a strong indication of a batch effect. For example, batches I and D, with low mean strength relative to the other batches, each contained one sample (I:b and D:c, respectively) that had high mean strength relative to the other samples. Also, batches H and C, with comparatively high mean batch strength, contain samples H:a and C:a with comparatively low mean sample strength. In Sect. [sec:TestingSig2is0] we will examine the need for incorporating batch-to-batch variability, in addition to sample-to-sample variability, in the statistical model. #### Nested Factors Because each level of occurs with one and only one level of we say that is nested within . Some presentations of mixed-effects models, especially those related to multilevel modeling or hierarchical linear models , leave the impression that one can only define random effects with respect to factors that are nested. This is the origin of the terms “multilevel”, referring to multiple, nested levels of variability, and “hierarchical”, also invoking the concept of a hierarchy of levels. To be fair, both those references do describe the use of models with random effects associated with non-nested factors, but such models tend to be treated as a special case. The blurring of mixed-effects models with the concept of multiple, hierarchical levels of variation results in an unwarranted emphasis on “levels” when defining a model and leads to considerable confusion. It is perfectly legitimate to define models having random effects associated with non-nested factors. The reasons for the emphasis on defining random effects with respect to nested factors only are that such cases do occur frequently in practice and that some of the computational methods for estimating the parameters in the models can only be easily applied to nested factors. This is not the case for the methods used in the package. Indeed there is nothing special done for models with random effects for nested factors. When random effects are associated with multiple factors exactly the same computational methods are used whether the factors form a nested sequence or are partially crossed or are completely crossed. There is, however, one aspect of nested grouping factors that we should emphasize, which is the possibility of a factor that is implicitly nested within another factor. Suppose, for example, that the factor was defined as having three levels instead of 30 with the implicit assumption that is nested within . It may seem silly to try to distinguish 30 different batches with only three levels of a factor but, unfortunately, data are frequently organized and presented like this, especially in text books. The factor in the data is exactly such an implicitly nested factor. If we cross-tabulate cask and batch  batch cask A B C D E F G H I J a 2 2 2 2 2 2 2 2 2 2 b 2 2 2 2 2 2 2 2 2 2 c 2 2 2 2 2 2 2 2 2 2 we get the impression that the and factors are crossed, not nested. If we know that the cask should be considered as nested within the batch then we should create a new categorical variable giving the batch-cask combination, which is exactly what the factor is. A simple way to create such a factor is to use the interaction operator, ‘’, on the factors. It is advisable, but not necessary, to apply to the result thereby dropping unused levels of the interaction from the set of all possible levels of the factor. (An “unused level” is a combination that does not occur in the data.) A convenient code idiom is or In a small data set like we can quickly detect a factor being implicitly nested within another factor and take appropriate action. In a large data set, perhaps hundreds of thousands of test scores for students in thousands of schools from hundreds of school districts, it is not always obvious if school identifiers are unique across the entire data set or just within a district. If you are not sure, the safest thing to do is to create the interaction factor, as shown above, so you can be confident that levels of the district:school interaction do indeed correspond to unique schools. ### Fitting a Model With Nested Random Effects Fitting a model with simple, scalar random effects for nested factors is done in exactly the same way as fitting a model with random effects for crossed grouping factors. We include random-effects terms for each factor, as in  In [14]: pstsm = fit!(lmm(strength ~ 1 + (1 | sample) + (1 | batch), pastes))   Out[14]: Linear mixed model fit by maximum likelihood Formula: strength ~ 1 + (1 | sample) + (1 | batch) logLik -2 logLik AIC BIC -123.99723 247.99447 255.99447 264.37184 Variance components: Column Variance Std.Dev. sample (Intercept) 8.4336168 2.90406902 batch (Intercept) 1.1991785 1.09507008 Residual 0.6780021 0.82340886 Number of obs: 60; levels of grouping factors: 30, 10 Fixed-effects parameters: Estimate Std.Error z value P(>|z|) (Intercept) 60.0533 0.642136 93.5212 <1e-99  Not only is the model specification similar for nested and crossed factors, the internal calculations are performed according to the methods described in Sect. [sec:definitions] for each model type. Comparing the patterns in the matrices$\Lambda$,$\mathbf Z'\mathbf Z$and$\mathbf L$for this model (Fig. [fig:fm04LambdaLimage]) to those in Fig. [fig:fm03LambdaLimage] shows that models with nested factors produce simple repeated structures along the diagonal of the sparse Cholesky factor,$\mathbf L$. This type of structure has the desirable property that there is no “fill-in” during calculation of the Cholesky factor. In other words, the number of non-zeros in$\mathbf L$is the same as the number of non-zeros in the lower triangle of the matrix being factored,$\Lambda'\mathbf Z'\mathbf Z\Lambda+\mathbf I$(which, because$\Lambda$is diagonal, has the same structure as$\mathbf Z'\mathbf Z$). ### Assessing Parameter Estimates in Model pstsm The parameter estimates are:$\widehat{\sigma_1}=$2.904, the standard deviation of the random effects for sample;$\widehat{\sigma_2}=$1.095, the standard deviation of the random effects for batch;$\widehat{\sigma}=$0.823, the standard deviation of the residual noise term; and$\widehat{\beta_0}=$60.053, the overall mean response, which is labeled (Intercept) in these models. The estimated standard deviation for sample is nearly three times as large as that for batch, which confirms what we saw in Fig. [fig:Pastesplot]. Indeed our conclusion from Fig. [fig:Pastesplot] was that there may not be a significant batch-to-batch variability in addition to the sample-to-sample variability. Plots of the prediction intervals of the random effects (Fig. [fig:fm04ranef]) [fig:fm04ranef] confirm this impression in that all the prediction intervals for the random effects for contain zero. Furthermore, a bootstrap sample  In [15]: srand(4321234) @time pstsbstp = bootstrap(10000, pstsm);   5.131384 seconds (8.78 M allocations: 209.211 MB, 1.82% gc time)   In [16]: plot(x = pstsbstp[:β₁], Geom.density(), Guide.xlabel("β₁"))   Out[16]: β₁ 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 51.0 51.2 51.4 51.6 51.8 52.0 52.2 52.4 52.6 52.8 53.0 53.2 53.4 53.6 53.8 54.0 54.2 54.4 54.6 54.8 55.0 55.2 55.4 55.6 55.8 56.0 56.2 56.4 56.6 56.8 57.0 57.2 57.4 57.6 57.8 58.0 58.2 58.4 58.6 58.8 59.0 59.2 59.4 59.6 59.8 60.0 60.2 60.4 60.6 60.8 61.0 61.2 61.4 61.6 61.8 62.0 62.2 62.4 62.6 62.8 63.0 63.2 63.4 63.6 63.8 64.0 64.2 64.4 64.6 64.8 65.0 65.2 65.4 65.6 65.8 66.0 66.2 66.4 66.6 66.8 67.0 67.2 67.4 67.6 67.8 68.0 68.2 68.4 68.6 68.8 69.0 50 55 60 65 70 51.0 51.5 52.0 52.5 53.0 53.5 54.0 54.5 55.0 55.5 56.0 56.5 57.0 57.5 58.0 58.5 59.0 59.5 60.0 60.5 61.0 61.5 62.0 62.5 63.0 63.5 64.0 64.5 65.0 65.5 66.0 66.5 67.0 67.5 68.0 68.5 69.0 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 -1 0 1 2 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 (function(N){var k=/[\.\/]/,L=/\s*,\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if("undefined"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if("undefined"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)"zIndex"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&& (e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],"zIndex"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d["*"] ],n=2;n--;)if(d= f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if("function"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null, [a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp("(?:\\.|\\/|^)"+k+"(?:\\.|\\/|$)")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if("*"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&
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A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind("snap.drag.*."+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\s*url$$(.+)$$/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;"svg"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z("filter");z(u,{id:k,filterUnits:"userSpaceOnUse"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on("snap.util.getattr.filter", function(){k.stop();var d=z(this.node,"filter");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on("snap.util.attr.filter",function(d){if(d instanceof v&&"filter"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&"none"!=d||(k.stop(),this.node.removeAttribute("filter"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation="{def}"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow= function(d,f,k,u,p){"string"==typeof k&&(p=u=k,k=4);"string"!=typeof u&&(p=u,u="#000");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||"#000");return a.format('<feGaussianBlur in="SourceAlpha" stdDeviation="{blur}"/><feOffset dx="{dx}" dy="{dy}" result="offsetblur"/><feFlood flood-color="{color}"/><feComposite in2="offsetblur" operator="in"/><feComponentTransfer><feFuncA type="linear" slope="{opacity}"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in="SourceGraphic"/></feMerge>', {color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type="matrix" values="{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia= function(d){null==d&&(d=1);return a.format('<feColorMatrix type="matrix" values="{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type="saturate" values="{amount}"/>',{amount:1- d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type="hueRotate" values="{angle}"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type="table" tableValues="{amount} {amount2}"/><feFuncG type="table" tableValues="{amount} {amount2}"/><feFuncB type="table" tableValues="{amount} {amount2}"/></feComponentTransfer>',{amount:d, amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type="linear" slope="{amount}"/><feFuncG type="linear" slope="{amount}"/><feFuncB type="linear" slope="{amount}"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type="linear" slope="{amount}" intercept="{amount2}"/><feFuncG type="linear" slope="{amount}" intercept="{amount2}"/><feFuncB type="linear" slope="{amount}" intercept="{amount2}"/></feComponentTransfer>', {amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C}); (function (glob, factory) { // AMD support if (typeof define === "function" && define.amd) { // Define as an anonymous module define("Gadfly", ["Snap.svg"], function (Snap) { return factory(Snap); }); } else { // Browser globals (glob is window) // Snap adds itself to window glob.Gadfly = factory(glob.Snap); } }(this, function (Snap) { var Gadfly = {}; // Get an x/y coordinate value in pixels var xPX = function(fig, x) { var client_box = fig.node.getBoundingClientRect(); return x * fig.node.viewBox.baseVal.width / client_box.width; }; var yPX = function(fig, y) { var client_box = fig.node.getBoundingClientRect(); return y * fig.node.viewBox.baseVal.height / client_box.height; }; Snap.plugin(function (Snap, Element, Paper, global) { // Traverse upwards from a snap element to find and return the first // note with the "plotroot" class. Element.prototype.plotroot = function () { var element = this; while (!element.hasClass("plotroot") && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.svgroot = function () { var element = this; while (element.node.nodeName != "svg" && element.parent() != null) { element = element.parent(); } return element; }; Element.prototype.plotbounds = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x0: bbox.x, x1: bbox.x + bbox.width, y0: bbox.y, y1: bbox.y + bbox.height }; }; Element.prototype.plotcenter = function () { var root = this.plotroot() var bbox = root.select(".guide.background").node.getBBox(); return { x: bbox.x + bbox.width / 2, y: bbox.y + bbox.height / 2 }; }; // Emulate IE style mouseenter/mouseleave events, since Microsoft always // does everything right. // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/ var events = ["mouseenter", "mouseleave"]; for (i in events) { (function (event_name) { var event_name = events[i]; Element.prototype[event_name] = function (fn, scope) { if (Snap.is(fn, "function")) { var fn2 = function (event) { if (event.type != "mouseover" && event.type != "mouseout") { return; } var reltg = event.relatedTarget ? event.relatedTarget : event.type == "mouseout" ? event.toElement : event.fromElement; while (reltg && reltg != this.node) reltg = reltg.parentNode; if (reltg != this.node) { return fn.apply(this, event); } }; if (event_name == "mouseenter") { this.mouseover(fn2, scope); } else { this.mouseout(fn2, scope); } } return this; }; })(events[i]); } Element.prototype.mousewheel = function (fn, scope) { if (Snap.is(fn, "function")) { var el = this; var fn2 = function (event) { fn.apply(el, [event]); }; } this.node.addEventListener( /Firefox/i.test(navigator.userAgent) ? "DOMMouseScroll" : "mousewheel", fn2); return this; }; // Snap's attr function can be too slow for things like panning/zooming. // This is a function to directly update element attributes without going // through eve. Element.prototype.attribute = function(key, val) { if (val === undefined) { return this.node.getAttribute(key); } else { this.node.setAttribute(key, val); return this; } }; Element.prototype.init_gadfly = function() { this.mouseenter(Gadfly.plot_mouseover) .mouseleave(Gadfly.plot_mouseout) .dblclick(Gadfly.plot_dblclick) .mousewheel(Gadfly.guide_background_scroll) .drag(Gadfly.guide_background_drag_onmove, Gadfly.guide_background_drag_onstart, Gadfly.guide_background_drag_onend); this.mouseenter(function (event) { init_pan_zoom(this.plotroot()); }); return this; }; }); // When the plot is moused over, emphasize the grid lines. Gadfly.plot_mouseover = function(event) { var root = this.plotroot(); var keyboard_zoom = function(event) { if (event.which == 187) { // plus increase_zoom_by_position(root, 0.1, true); } else if (event.which == 189) { // minus increase_zoom_by_position(root, -0.1, true); } }; root.data("keyboard_zoom", keyboard_zoom); window.addEventListener("keyup", keyboard_zoom); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); xgridlines.data("unfocused_strokedash", xgridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
ygridlines.data("unfocused_strokedash",
ygridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2")); // emphasize grid lines var destcolor = root.data("focused_xgrid_color"); xgridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("focused_ygrid_color"); ygridlines.attribute("stroke-dasharray", "none") .selectAll("path") .animate({stroke: destcolor}, 250); // reveal zoom slider root.select(".zoomslider") .animate({opacity: 1.0}, 250); }; // Reset pan and zoom on double click Gadfly.plot_dblclick = function(event) { set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0); }; // Unemphasize grid lines on mouse out. Gadfly.plot_mouseout = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("keyboard_zoom")); root.data("keyboard_zoom", undefined); var xgridlines = root.select(".xgridlines"), ygridlines = root.select(".ygridlines"); var destcolor = root.data("unfocused_xgrid_color"); xgridlines.attribute("stroke-dasharray", xgridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); destcolor = root.data("unfocused_ygrid_color"); ygridlines.attribute("stroke-dasharray", ygridlines.data("unfocused_strokedash")) .selectAll("path") .animate({stroke: destcolor}, 250); // hide zoom slider root.select(".zoomslider") .animate({opacity: 0.0}, 250); }; var set_geometry_transform = function(root, tx, ty, scale) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var old_scale = root.data("scale"); var xscale = xscalable ? scale : 1.0, yscale = yscalable ? scale : 1.0; tx = xscalable ? tx : 0.0; ty = yscalable ? ty : 0.0; var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale); root.selectAll(".geometry, image") .forEach(function (element, i) { element.transform(t); }); bounds = root.plotbounds(); if (yscalable) { var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale); root.selectAll(".xfixed") .forEach(function (element, i) { element.transform(xfixed_t); }); root.select(".ylabels") .transform(xfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1, 1/scale, cx, cy).add(st); element.transform(unscale_t); var y = cy * scale + ty; element.attr("visibility", bounds.y0 <= y && y <= bounds.y1 ? "visible" : "hidden"); } }); } if (xscalable) { var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0); var xtrans = new Snap.Matrix().translate(tx, 0); root.selectAll(".yfixed") .forEach(function (element, i) { element.transform(yfixed_t); }); root.select(".xlabels") .transform(yfixed_t) .selectAll("text") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var cx = element.asPX("x"), cy = element.asPX("y"); var st = element.data("static_transform"); unscale_t = new Snap.Matrix(); unscale_t.scale(1/scale, 1, cx, cy).add(st); element.transform(unscale_t); var x = cx * scale + tx; element.attr("visibility", bounds.x0 <= x && x <= bounds.x1 ? "visible" : "hidden"); } }); } // we must unscale anything that is scale invariance: widths, raiduses, etc. var size_attribs = ["font-size"]; var unscaled_selection = ".geometry, .geometry *"; if (xscalable) { size_attribs.push("rx"); unscaled_selection += ", .xgridlines"; } if (yscalable) { size_attribs.push("ry"); unscaled_selection += ", .ygridlines"; } root.selectAll(unscaled_selection) .forEach(function (element, i) { // circle need special help if (element.node.nodeName == "circle") { var cx = element.attribute("cx"), cy = element.attribute("cy"); unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale, cx, cy); element.transform(unscale_t); return; } for (i in size_attribs) { var key = size_attribs[i]; var val = parseFloat(element.attribute(key)); if (val !== undefined && val != 0 && !isNaN(val)) { element.attribute(key, val * old_scale / scale); } } }); }; // Find the most appropriate tick scale and update label visibility. var update_tickscale = function(root, scale, axis) { if (!root.hasClass(axis + "scalable")) return; var tickscales = root.data(axis + "tickscales"); var best_tickscale = 1.0; var best_tickscale_dist = Infinity; for (tickscale in tickscales) { var dist = Math.abs(Math.log(tickscale) - Math.log(scale)); if (dist < best_tickscale_dist) { best_tickscale_dist = dist; best_tickscale = tickscale; } } if (best_tickscale != root.data(axis + "tickscale")) { root.data(axis + "tickscale", best_tickscale); var mark_inscale_gridlines = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; var mark_inscale_labels = function (element, i) { var inscale = element.attr("gadfly:scale") == best_tickscale; element.attribute("gadfly:inscale", inscale); element.attr("visibility", inscale ? "visible" : "hidden"); }; root.select("." + axis + "gridlines").selectAll("path").forEach(mark_inscale_gridlines); root.select("." + axis + "labels").selectAll("text").forEach(mark_inscale_labels); } }; var set_plot_pan_zoom = function(root, tx, ty, scale) { var old_scale = root.data("scale"); var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; // compute the viewport derived from tx, ty, and scale var x_min = -width * scale - (scale * width - width), x_max = width * scale, y_min = -height * scale - (scale * height - height), y_max = height * scale; var x0 = bounds.x0 - scale * bounds.x0, y0 = bounds.y0 - scale * bounds.y0; var tx = Math.max(Math.min(tx - x0, x_max), x_min), ty = Math.max(Math.min(ty - y0, y_max), y_min); tx += x0; ty += y0; // when the scale change, we may need to alter which set of // ticks is being displayed if (scale != old_scale) { update_tickscale(root, scale, "x"); update_tickscale(root, scale, "y"); } set_geometry_transform(root, tx, ty, scale); root.data("scale", scale); root.data("tx", tx); root.data("ty", ty); }; var scale_centered_translation = function(root, scale) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var tx0 = root.data("tx"), ty0 = root.data("ty"); var scale0 = root.data("scale"); // how off from center the current view is var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2), yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2); // rescale offsets xoff = xoff * scale / scale0; yoff = yoff * scale / scale0; // adjust for the panel position being scaled var x_edge_adjust = bounds.x0 * (1 - scale), y_edge_adjust = bounds.y0 * (1 - scale); return { x: xoff + x_edge_adjust + (width - width * scale) / 2, y: yoff + y_edge_adjust + (height - height * scale) / 2 }; }; // Initialize data for panning zooming if it isn't already. var init_pan_zoom = function(root) { if (root.data("zoompan-ready")) { return; } // The non-scaling-stroke trick. Rather than try to correct for the // stroke-width when zooming, we force it to a fixed value. var px_per_mm = root.node.getCTM().a; // Drag events report deltas in pixels, which we'd like to convert to // millimeters. root.data("px_per_mm", px_per_mm); root.selectAll("path") .forEach(function (element, i) { sw = element.asPX("stroke-width") * px_per_mm; if (sw > 0) { element.attribute("stroke-width", sw); element.attribute("vector-effect", "non-scaling-stroke"); } }); // Store ticks labels original tranformation root.selectAll(".xlabels > text, .ylabels > text") .forEach(function (element, i) { var lm = element.transform().localMatrix; element.data("static_transform", new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f)); }); var xgridlines = root.select(".xgridlines"); var ygridlines = root.select(".ygridlines"); var xlabels = root.select(".xlabels"); var ylabels = root.select(".ylabels"); if (root.data("tx") === undefined) root.data("tx", 0); if (root.data("ty") === undefined) root.data("ty", 0); if (root.data("scale") === undefined) root.data("scale", 1.0); if (root.data("xtickscales") === undefined) { // index all the tick scales that are listed var xtickscales = {}; var ytickscales = {}; var add_x_tick_scales = function (element, i) { xtickscales[element.attribute("gadfly:scale")] = true; }; var add_y_tick_scales = function (element, i) { ytickscales[element.attribute("gadfly:scale")] = true; }; if (xgridlines) xgridlines.selectAll("path").forEach(add_x_tick_scales); if (ygridlines) ygridlines.selectAll("path").forEach(add_y_tick_scales); if (xlabels) xlabels.selectAll("text").forEach(add_x_tick_scales); if (ylabels) ylabels.selectAll("text").forEach(add_y_tick_scales); root.data("xtickscales", xtickscales); root.data("ytickscales", ytickscales); root.data("xtickscale", 1.0); } var min_scale = 1.0, max_scale = 1.0; for (scale in xtickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } for (scale in ytickscales) { min_scale = Math.min(min_scale, scale); max_scale = Math.max(max_scale, scale); } root.data("min_scale", min_scale); root.data("max_scale", max_scale); // store the original positions of labels if (xlabels) { xlabels.selectAll("text") .forEach(function (element, i) { element.data("x", element.asPX("x")); }); } if (ylabels) { ylabels.selectAll("text") .forEach(function (element, i) { element.data("y", element.asPX("y")); }); } // mark grid lines and ticks as in or out of scale. var mark_inscale = function (element, i) { element.attribute("gadfly:inscale", element.attribute("gadfly:scale") == 1.0); }; if (xgridlines) xgridlines.selectAll("path").forEach(mark_inscale); if (ygridlines) ygridlines.selectAll("path").forEach(mark_inscale); if (xlabels) xlabels.selectAll("text").forEach(mark_inscale); if (ylabels) ylabels.selectAll("text").forEach(mark_inscale); // figure out the upper ond lower bounds on panning using the maximum // and minum grid lines var bounds = root.plotbounds(); var pan_bounds = { x0: 0.0, y0: 0.0, x1: 0.0, y1: 0.0 }; if (xgridlines) { xgridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.x1 - bbox.x < pan_bounds.x0) { pan_bounds.x0 = bounds.x1 - bbox.x; } if (bounds.x0 - bbox.x > pan_bounds.x1) { pan_bounds.x1 = bounds.x0 - bbox.x; } element.attr("visibility", "visible"); } }); } if (ygridlines) { ygridlines .selectAll("path") .forEach(function (element, i) { if (element.attribute("gadfly:inscale") == "true") { var bbox = element.node.getBBox(); if (bounds.y1 - bbox.y < pan_bounds.y0) { pan_bounds.y0 = bounds.y1 - bbox.y; } if (bounds.y0 - bbox.y > pan_bounds.y1) { pan_bounds.y1 = bounds.y0 - bbox.y; } element.attr("visibility", "visible"); } }); } // nudge these values a little pan_bounds.x0 -= 5; pan_bounds.x1 += 5; pan_bounds.y0 -= 5; pan_bounds.y1 += 5; root.data("pan_bounds", pan_bounds); root.data("zoompan-ready", true) }; // drag actions, i.e. zooming and panning var pan_action = { start: function(root, x, y, event) { root.data("dx", 0); root.data("dy", 0); root.data("tx0", root.data("tx")); root.data("ty0", root.data("ty")); }, update: function(root, dx, dy, x, y, event) { var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var tx0 = root.data("tx"), ty0 = root.data("ty"); var dx0 = root.data("dx"), dy0 = root.data("dy"); root.data("dx", dx); root.data("dy", dy); dx = dx - dx0; dy = dy - dy0; var tx = tx0 + dx, ty = ty0 + dy; set_plot_pan_zoom(root, tx, ty, root.data("scale")); }, end: function(root, event) { }, cancel: function(root) { set_plot_pan_zoom(root, root.data("tx0"), root.data("ty0"), root.data("scale")); } }; var zoom_box; var zoom_action = { start: function(root, x, y, event) { var bounds = root.plotbounds(); var width = bounds.x1 - bounds.x0, height = bounds.y1 - bounds.y0; var ratio = width / height; var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); x = xscalable ? x / px_per_mm : bounds.x0; y = yscalable ? y / px_per_mm : bounds.y0; var w = xscalable ? 0 : width; var h = yscalable ? 0 : height; zoom_box = root.rect(x, y, w, h).attr({ "fill": "#000", "opacity": 0.25 }); zoom_box.data("ratio", ratio); }, update: function(root, dx, dy, x, y, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var px_per_mm = root.data("px_per_mm"); var bounds = root.plotbounds(); if (yscalable) { y /= px_per_mm; y = Math.max(bounds.y0, y); y = Math.min(bounds.y1, y); } else { y = bounds.y1; } if (xscalable) { x /= px_per_mm; x = Math.max(bounds.x0, x); x = Math.min(bounds.x1, x); } else { x = bounds.x1; } dx = x - zoom_box.attr("x"); dy = y - zoom_box.attr("y"); if (xscalable && yscalable) { var ratio = zoom_box.data("ratio"); var width = Math.min(Math.abs(dx), ratio * Math.abs(dy)); var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio); dx = width * dx / Math.abs(dx); dy = height * dy / Math.abs(dy); } var xoffset = 0, yoffset = 0; if (dx < 0) { xoffset = dx; dx = -1 * dx; } if (dy < 0) { yoffset = dy; dy = -1 * dy; } if (isNaN(dy)) { dy = 0.0; } if (isNaN(dx)) { dx = 0.0; } zoom_box.transform("T" + xoffset + "," + yoffset); zoom_box.attr("width", dx); zoom_box.attr("height", dy); }, end: function(root, event) { var xscalable = root.hasClass("xscalable"), yscalable = root.hasClass("yscalable"); var zoom_bounds = zoom_box.getBBox(); if (zoom_bounds.width * zoom_bounds.height <= 0) { return; } var plot_bounds = root.plotbounds(); var zoom_factor = 1.0; if (yscalable) { zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height; } else { zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width; } var tx = (root.data("tx") - zoom_bounds.x) * zoom_factor + plot_bounds.x0, ty = (root.data("ty") - zoom_bounds.y) * zoom_factor + plot_bounds.y0; set_plot_pan_zoom(root, tx, ty, root.data("scale") * zoom_factor); zoom_box.remove(); }, cancel: function(root) { zoom_box.remove(); } }; Gadfly.guide_background_drag_onstart = function(x, y, event) { var root = this.plotroot(); var scalable = root.hasClass("xscalable") || root.hasClass("yscalable"); var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable; var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable; var drag_action = zoomable ? zoom_action : panable ? pan_action : undefined; root.data("drag_action", drag_action); if (drag_action) { var cancel_drag_action = function(event) { if (event.which == 27) { // esc key drag_action.cancel(root); root.data("drag_action", undefined); } }; window.addEventListener("keyup", cancel_drag_action); root.data("cancel_drag_action", cancel_drag_action); drag_action.start(root, x, y, event); } }; Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.update(root, dx, dy, x, y, event); } }; Gadfly.guide_background_drag_onend = function(event) { var root = this.plotroot(); window.removeEventListener("keyup", root.data("cancel_drag_action")); root.data("cancel_drag_action", undefined); var drag_action = root.data("drag_action"); if (drag_action) { drag_action.end(root, event); } root.data("drag_action", undefined); }; Gadfly.guide_background_scroll = function(event) { if (event.shiftKey) { increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta); event.preventDefault(); } }; Gadfly.zoomslider_button_mouseover = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseover_color")}, 100); }; Gadfly.zoomslider_button_mouseout = function(event) { this.select(".button_logo") .animate({fill: this.data("mouseout_color")}, 100); }; Gadfly.zoomslider_zoomout_click = function(event) { increase_zoom_by_position(this.plotroot(), -0.1, true); }; Gadfly.zoomslider_zoomin_click = function(event) { increase_zoom_by_position(this.plotroot(), 0.1, true); }; Gadfly.zoomslider_track_click = function(event) { // TODO }; // Map slider position x to scale y using the function y = a*exp(b*x)+c. // The constants a, b, and c are solved using the constraint that the function // should go through the points (0; min_scale), (0.5; 1), and (1; max_scale). var scale_from_slider_position = function(position, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return a * Math.exp(b * position) + c; } // inverse of scale_from_slider_position var slider_position_from_scale = function(scale, min_scale, max_scale) { var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2), b = 2 * Math.log((max_scale - 1) / (1 - min_scale)), c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2); return 1 / b * Math.log((scale - c) / a); } var increase_zoom_by_position = function(root, delta_position, animate) { var scale = root.data("scale"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var position = slider_position_from_scale(scale, min_scale, max_scale); position += delta_position; scale = scale_from_slider_position(position, min_scale, max_scale); set_zoom(root, scale, animate); } var set_zoom = function(root, scale, animate) { var min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("scale"); var new_scale = Math.max(min_scale, Math.min(scale, max_scale)); if (animate) { Snap.animate( old_scale, new_scale, function (new_scale) { update_plot_scale(root, new_scale); }, 200); } else { update_plot_scale(root, new_scale); } } var update_plot_scale = function(root, new_scale) { var trans = scale_centered_translation(root, new_scale); set_plot_pan_zoom(root, trans.x, trans.y, new_scale); root.selectAll(".zoomslider_thumb") .forEach(function (element, i) { var min_pos = element.data("min_pos"), max_pos = element.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"); var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(new_scale, min_scale, max_scale); element.transform(new Snap.Matrix().translate( Math.max(min_pos, Math.min( max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0)); }); }; Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) { var root = this.plotroot(); var min_pos = this.data("min_pos"), max_pos = this.data("max_pos"), min_scale = root.data("min_scale"), max_scale = root.data("max_scale"), old_scale = root.data("old_scale"); var px_per_mm = root.data("px_per_mm"); dx /= px_per_mm; dy /= px_per_mm; var xmid = (min_pos + max_pos) / 2; var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) + dx / (max_pos - min_pos); // compute the new scale var new_scale = scale_from_slider_position(xpos, min_scale, max_scale); new_scale = Math.min(max_scale, Math.max(min_scale, new_scale)); update_plot_scale(root, new_scale); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragstart = function(x, y, event) { this.animate({fill: this.data("mouseover_color")}, 100); var root = this.plotroot(); // keep track of what the scale was when we started dragging root.data("old_scale", root.data("scale")); event.stopPropagation(); }; Gadfly.zoomslider_thumb_dragend = function(event) { this.animate({fill: this.data("mouseout_color")}, 100); event.stopPropagation(); }; var toggle_color_class = function(root, color_class, ison) { var guides = root.selectAll(".guide." + color_class + ",.guide ." + color_class); var geoms = root.selectAll(".geometry." + color_class + ",.geometry ." + color_class); if (ison) { guides.animate({opacity: 0.5}, 250); geoms.animate({opacity: 0.0}, 250); } else { guides.animate({opacity: 1.0}, 250); geoms.animate({opacity: 1.0}, 250); } }; Gadfly.colorkey_swatch_click = function(event) { var root = this.plotroot(); var color_class = this.data("color_class"); if (event.shiftKey) { root.selectAll(".colorkey text") .forEach(function (element) { var other_color_class = element.data("color_class"); if (other_color_class != color_class) { toggle_color_class(root, other_color_class, element.attr("opacity") == 1.0); } }); } else { toggle_color_class(root, color_class, this.attr("opacity") == 1.0); } }; return Gadfly; })); //@ sourceURL=gadfly.js (function (glob, factory) { // AMD support if (typeof require === "function" && typeof define === "function" && define.amd) { require(["Snap.svg", "Gadfly"], function (Snap, Gadfly) { factory(Snap, Gadfly); }); } else { factory(glob.Snap, glob.Gadfly); } })(window, function (Snap, Gadfly) { var fig = Snap("#img-2c848d32"); fig.select("#img-2c848d32-5") .init_gadfly(); fig.select("#img-2c848d32-7") .plotroot().data("unfocused_ygrid_color", "#D0D0E0") ; fig.select("#img-2c848d32-7") .plotroot().data("focused_ygrid_color", "#A0A0A0") ; fig.select("#img-2c848d32-8") .plotroot().data("unfocused_xgrid_color", "#D0D0E0") ; fig.select("#img-2c848d32-8") .plotroot().data("focused_xgrid_color", "#A0A0A0") ; fig.select("#img-2c848d32-12") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-2c848d32-12") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-2c848d32-12") .click(Gadfly.zoomslider_zoomin_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; fig.select("#img-2c848d32-14") .data("max_pos", 120.42) ; fig.select("#img-2c848d32-14") .data("min_pos", 103.42) ; fig.select("#img-2c848d32-14") .click(Gadfly.zoomslider_track_click); fig.select("#img-2c848d32-15") .data("max_pos", 120.42) ; fig.select("#img-2c848d32-15") .data("min_pos", 103.42) ; fig.select("#img-2c848d32-15") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-2c848d32-15") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-2c848d32-15") .drag(Gadfly.zoomslider_thumb_dragmove, Gadfly.zoomslider_thumb_dragstart, Gadfly.zoomslider_thumb_dragend) ; fig.select("#img-2c848d32-16") .data("mouseover_color", "#CD5C5C") ; fig.select("#img-2c848d32-16") .data("mouseout_color", "#6A6A6A") ; fig.select("#img-2c848d32-16") .click(Gadfly.zoomslider_zoomout_click) .mouseenter(Gadfly.zoomslider_button_mouseover) .mouseleave(Gadfly.zoomslider_button_mouseout) ; });   In [17]: plot(x = pstsbstp[:σ], Geom.density(), Guide.xlabel("σ"))   Out[17]: σ -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -1.50 -1.45 -1.40 -1.35 -1.30 -1.25 -1.20 -1.15 -1.10 -1.05 -1.00 -0.95 -0.90 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85 2.90 2.95 3.00 -2 0 2 4 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 -4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 -5 0 5 10 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 (function(N){var k=/[\.\/]/,L=/\s*,\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if("undefined"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if("undefined"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)"zIndex"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&& (e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],"zIndex"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d["*"] ],n=2;n--;)if(d= f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if("function"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null, [a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp("(?:\\.|\\/|^)"+k+"(?:\\.|\\/|$)")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if("*"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&
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