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46

The idea behind this solution is to construct a superposition of Gaussian surfaces whose amplitude decay in time, and use DensityPlot to plot the trail: trail[fun_, {t_, tmin_, tmax_, dt_}, k_, lam_][xxx_, yyy_] := Module[{trange, xrange, yrange, twindow, trailf, sel, decayf}, decayf[x0_, y0_, t0_] := Exp[-k t0 - lam^2 (x0^2 + y0^2)]; twindow = 6/k; ...


39

Solution 1: Using 3D Texture with Polygons The idea is to use Polygon with 3D texture supported by Texture, but it requires a bit of undocumented hack to make it smooth. The original data set is from Stanford Graphics Group website. The dataset that has been used is CThead, 8-bit tiffs (download). Before proceed, make sure that you have a plenty of memory ...


37

Here is a simple approach to create a ghost trail: obj[{xfunc_, yfunc_}, rad_, lag_, npts_][x_] := MapThread[ {Opacity[#1, ColorData["SunsetColors", #1]], Disk[{xfunc@#2, yfunc@#2}, rad Exp[#1 - 1]]} &, Through[{Rescale, Identity}[Range[x - lag, x, lag/npts]]]] frames = Most@Table[Graphics[obj[{Sin[2 #] &, Sin[3 #] &}, 0.1, 1, 500][u], ...


35

The undocumented System`Dump`showStringDiff function neatly does the diff and highlights it for you. The simplest usage is: System`Dump`showStringDiff[text1, text2] You can choose custom colours for the highlights with the Styles option. You can also change the background, font weight, add a strikethrough, etc.: System`Dump`showStringDiff[text1, text2, ...


30

If what you want to visualize is how good the fit is, then you should do as @whuber suggests and plot the residuals, that is, the difference between the data and the fitted function. Below, each data point is drawn as a point with area proportional to the magnitude of the residual. Red means that the data value is higher than the fit; blue means the data is ...


25

Yet another method: Let us calculate values of function on appropriate rectangular grids, which we will convert to textures (1 pixel = 1 value). Interpolation between pixels is built-in. f = 2 #1^2 + 2 #2^2 + #3^2 + #1 #2 &; PolyhedronData["Cube"] // N // Normal // toTriangles // texturize[f, 50, Hue, Lighting -> "Neutral", Axes -> True] ...


23

Based on that outdated notebook, I did the following function: VennDiagram2[n_, ineqs_: {}] := Module[{i, r = .6, R = 1, v, grouprules, x, y, x1, x2, y1, y2, ve}, v = Table[Circle[r {Cos[#], Sin[#]} &[2 Pi (i - 1)/n], R], {i, n}]; {x1, x2} = {Min[#], Max[#]} &[ Flatten@Replace[v, Circle[{xx_, yy_}, rr_] :> {xx - rr, xx + rr}, ...


22

Edit: I added more explanations below, because this visualization method is quite different from conventional vector plots For just this purpose I had at some point invented the following visualization technique. I'll reproduce your definition first. It defines a complex vector field on the surface of a unit sphere. Clear[\[Epsilon]];(*Polarization ...


22

One can also use MeshFunctions: Clear[f]; f = {x, y, z} \[Function] x + Sin[5 z] + y^2; cube = PolyhedronData["Cube", "RegionFunction"]; mesh = 15; RegionPlot3D[cube[x/2, y/2, z/2], {x, -1, 1}, {y, -1, 1}, {z, -1, 1}, MeshFunctions -> {f}, Mesh -> mesh, MeshShading -> ColorData["Rainbow"] /@ Range[0, 1, 1/(mesh + 1)], PlotPoints -> 50, ...


21

With a bit of blur, but still not the variable-width blur in the example. obj[{xfunc_, yfunc_}, rad_, lag_, npts_][x_] := With[{trail = Range[x - lag, x, lag/npts]}, {ColorData["SunsetColors"]@#1, Opacity@#1, Disk[{xfunc@#2, yfunc@#2}, rad]} & @@@ Transpose[{Rescale[trail], trail}]] frames = Most@ Table[ImageCompose[# ~Blur~ 4, ...


21

What about perceived co-operative strength (well, at least something derived from counting the times two candidates were mentioned together): t = 1/Table[ Count[votes, _?(MemberQ[#, i] && MemberQ[#, j] &)], {i, 6}, {j, 6}]/. ComplexInfinity -> DirectedInfinity[1] // Quiet Do[t[[i, i]] = DirectedInfinity[1], {i, ...


21

With small tables of values, complex graphics can obscure the data. Ed Tufte has recommended just showing the counts. He also points out the worth of presenting the values in a meaningful order: here, the rows go from first to third place while the columns are (roughly) in order of the standings. raw = Import[ ...


21

This could provide a good starting point, since the structure of the diagrams is simply a cross with four regions that themselves can contain similar crosses, you can simply define a structure to represent this nesting and a recursive function to draw such structures. In my implementation I just use the head c to indicate a cross: dirs = {{1, 0}, {0, 1}, ...


18

Here's a start (perhaps it's better to say continuation since you've already gotten started): Row@Flatten[sa /. {a_, b_} :> { Style[a, Red], "(", Style[b, Green], ")"}] By capturing the word fragmentth to the left and right of a, you thhould be able to end up with thomething more like:


18

Take $r=1, t=5, d=10$ for example: r = 1; t = 5; d = 10; The parametric equation for the 3-torus is given by: torus3 = {(r + (t + d Cos[a]) Cos[b]) Cos[c], (r + (t + d Cos[a]) Cos[b]) Sin[c], (t + d Cos[a]) Sin[b], d Sin[a]}; Suppose the plane is determined by its normal $\mathbf n$ and a point $\mathbf o$ on it: \[DoubleStruckN] = ...


18

If you're stuck with the terminal, but have access to X11 and Java, then I suggest using JavaGraphics`, which allows you to display plots, but continue to work in the terminal. This was also answered here, but I learnt it from from Jens. If you really want an ASCII plot, I suggest using the Terminal` package that gives you an ASCII plot: << Terminal` ...


18

It does seem that the options PeriodicInterpolation -> True and Method -> "Spline" are incompatible, so I'll give a method for implementing a genuine cubic periodic spline for curves. First, let's talk about parametrizing the curve. Eugene Lee, in this paper, introduced what is known as centripetal parametrization that can be used when one wants to ...


18

This site has exactly what you want here, already in Mathematica code. One example here:


18

With the set-up you already have, you can do nearbin = Nearest[Table[verttri[[i]] -> i, {i, Length@verttri}]]; counts = BinCounts[nearbin /@ data, {1, Length@verttri + 1, 1}]; which counts the number of data points nearest to each vertex. Then just draw the glyphs directly: With[{maxCount = Max@counts}, Graphics[ Table[Disk[verttri[[i]], 0.5 ...


18

One key function you might need is the (undocumented) function Graphics`Mesh`InPolygonQ[], which tests if a point is inside a given polygon. With it, and a few other tweaks, here's my version of weatherMap[]: weatherMap[region_String, property_String, res_Integer: 25, opts___] := Module[{fmin, cmax, coords, pts, minLong, maxLong, minLat, maxLat, ...


17

Let's do real world application. Give the members of the Dow Jones Industrial Average: mem = FinancialData["^DJI", "Members"] {"AA", "AXP", "BA", "BAC", "CAT", "CSCO", "CVX", "DD", "DIS", "GE", "HD", "HPQ", "IBM", "INTC", "JNJ", "JPM", "KFT", "KO", "MCD", "MMM", "MRK", "MSFT", "PFE", "PG", "T", "TRV", "UTX", "VZ", "WMT", "XOM"} Get ...


17

tl;dr Final results first: (*Function Definition*) ClearAll[opaFun]; Options[opaFun] = Options[ListPlot]; opaFun[points_, opts : OptionsPattern[]] := Module[{f, steps = 10 }, f[x_] := Min[Norm /@ Flatten[ImageData@ ListPlot[points, opts, Axes -> False, PlotStyle -> {Black, Opacity[x]}],1]]/Sqrt@3; Return@NestWhileList[{#, f[#]} ...


17

I would try plt=Show[ListPointPlot3D[data, ColorFunction -> "Rainbow"], Plot3D[fit["BestFit"], {x, 0, 180}, {y, 0, 0.1}, PlotStyle -> Directive[Yellow, Specularity[White, 20], Opacity[0.3]]], BoxRatios -> {1, 1, 1}] Then you can change perspective? GraphicsArray[{{plt, Show[plt, ViewPoint -> Front]}, {Show[plt, ViewPoint ...


17

I like to draw the predicted and actual responses and connect them with a little line. That shows where the fit is good and where it isn't. With[{ actualpredicted={ data, Transpose[ Append[ Transpose[ fit["Data"][[All,{1,2}]]], fit["PredictedResponse"] ] ] } }, Show[ ListPointPlot3D[actualpredicted, ...


17

It seems networkx uses the D3 library and the example is based on this. We can adapt that code to work with Mathematica and generate JSON output from Mathematica. Save the HTML from the linked page to index.html. Change miserables.json in the source code to graph.json. Generate JSON with Mathematica: g = RandomGraph[BarabasiAlbertGraphDistribution[100, ...


17

To visualize directional information between the two signals, it is sufficient to cross-correlate them and look at the time lags. If the signal in, say, microphone A lags that in microphone B, then it implies that the source was closer to B than A. As you move around the room, the lag should change appropriately depending on the sine/cosine (depending on the ...


17

Something to get you started? f[{x_, y_}] := -Cos[x] x^2 - y^2 xy = First[ ContourPlot[f[{x, y}], {x, -1, 1}, {y, -1, 1}]] /. {x_?AtomQ, y_?AtomQ} :> {x, y, 1}; xz = First[ ContourPlot[f[{x, y}], {x, -1, 1}, {y, -1, 1}]] /. {x_?AtomQ, y_?AtomQ} :> {x, -1, y}; yz = First[ ContourPlot[f[{x, y}], {x, -1, 1}, {y, -1, 1}]] /. ...


17

I don't have time to do the full-monty on the question, but perhaps this little-known functionality might be of use: Needs["MultivariateStatistics`"] (* fake some data *) data = RandomVariate[BinormalDistribution[{20, 20}, {5, 5}, .75], 500]; Show[{ListPlot[data, PlotRange -> Automatic, AspectRatio -> 1], Graphics[{Red, ...


16

With this function a random integer is inserted in the e-mail address (gmailuser@gmail.com becomes gmailuser+randominteger@gmail.com) and then the hash value is computed. The hash value is used to get the corresponding identicon from the Gravatar website. This approach can address also some privacy concerns. generatePic[email_] := Module[{emailparts, ...


16

You get nice Venn diagrams using W|A, eg.: = (A inter B) un (C inter D) the inter is esc inter esc and the un is esc un esc or skipping the opening = which doesn't work in the midst of a program: WolframAlpha["(A \[Intersection] B) \[Union] (C \[Intersection] D)", \ {{"VennDiagram", 1}, "Content"}]



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