# Tag Info

55

TL;DR: package at the bottom of post. UPDATES 6: Tiny update: Import can now use the ".bvh" extension to determine the import type. The code that does this is ugly, but I don't see any other way at the moment. out = Import["C:\\Female1_C03_Run.bvh"] 5: Added error checking and registered the package as an official importer for "BVH" files, so ...

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], ...

36

Here's a way to morph the boundaries. After finding the boundaries by Thinning of the result of EdgeDetect, FindCurvePath finds a sequence of points that traces a path around each segment. MorphologicalComponents numbers the component left to right, top to bottom, so that 1 is the apple leaf, 2 is the i-dot, 3 is the apple body, and 4 is the i-stem (5, 6 ...

35

The undocumented SystemDumpshowStringDiff function neatly does the diff and highlights it for you. The simplest usage is: SystemDumpshowStringDiff[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.: SystemDumpshowStringDiff[text1, text2, ...

31

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 ...

30

Let you have a function and an initial point f[x_] := Cos[x] x0 = 0.2; Then you can calculate a sequence seq = NestList[f, x0, 10] (* {0.2, 0.980067, 0.556967, 0.848862, 0.660838, 0.789478, \ 0.704216, 0.76212, 0.723374, 0.749577, 0.731977} *) and vizualize it with a so-called Cobweb plot p = Join @@ ({{#, #}, {##}} & @@@ Partition[seq, 2, 1]); ...

25

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}, ...

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] ...

24

You can also calculate the Coefficient of Determination, R Squared. This is the same as the correlation squared, but by making use of LinearModelFit you can create some additional graphics. To make a sample distribution you can use this: CreateDistribution[] := DynamicModule[{savepts = {{-1, -1}}}, Dynamic[ EventHandler[ ListPlot[pts, AxesOrigin ...

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}, ...

19

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

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

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"}]

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

Basic, but useful options Something that certainly helps in representing your data is to set the range of x and y to be equal: ListPlot[data, PlotRange -> {{0,3},{0,3}}, AxesLabel -> {X, Y}] Moreover, if the slope is also important you should set the aspect ratio to 1: ListPlot[data, PlotRange -> {{0,3},{0,3}}, AxesLabel -> {X, Y}, ...

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

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 ...

18

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, ...

18

One key function you might need is the (undocumented) function GraphicsMesh`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, ...

18

One way to do it would be to use glyphs. We can extract the curves that make up the two characters as follows: a = First@First@Last@First@First@ ImportString[ExportString[ Style[FromCharacterCode[61440], 24, FontFamily -> "Baskerville Old Face"], "PDF"], "PDF", "TextMode" -> "Outlines"]; b = First@First@Last@First@First@ ...

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 ...

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