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15

Update According to KennyColnago's advice, post-processing is not needed, as StreamColorFunction can handle it essentially by using VertexColors on Line-s: ListStreamPlot[ testdata, StreamPoints -> {samplePoints, Automatic, 10}, StreamStyle -> "Line", Background -> Black, ...


5

I'm sure there are lots of things one might try -- here is an attempt using some of the built in options that are available in ListStreamPlot: Show[Graphics[{Black, CountryData["USA", "Polygon"]}], ListStreamPlot[ Table[{{x, y}, Through[{Cos, Sin}[ WeatherData[{y, x}, "WindDirection"]]]}, {x, -140, -70, 5}, {y, 24, 50, 5}], StreamScale ...


11

Just a combination of Graphics3D objects Graphics3D[{Scale[ Cylinder[{{0, 0.9, -0.5}, {2, 0.7, 0.5}}, 0.75], {1, 0.95, 1}], Scale[Cylinder[{{0, -0.9, 0}, {2, -0.7, 0}}, 0.75], {1.0, 0.95, 1}], Scale[Cylinder[{{-1.1, 0, 0}, {-0.3, 0, 0}}, 1.5], {1, 1, 0.5}], Scale[Sphere[{0., 0.75, -0.25}, 1.05], {1.1, 0.9, 1}], Scale[Sphere[{0., -0.75, 0.1}, 1.05], {1.1, ...


313

I have to confess that I see this as a proper challenge, as I am usually quite creative in finding/combining functions to provide a desired behavior. So I will give it another try. which is generated using box[x_, x1_, x2_, a_, b_] := Tanh[a (x - x1)] + Tanh[-b (x - x2)]; ex[z_, z0_, s_] := Exp[-(z - z0)^2/s] and r[z_, x_] := (*body*).4 (1.0 - .4 ...


88

Parametric Buttocks Manipulator Manipulate[ ParametricPlot3D[{ (e u^p + (1 + (c - a u) (u - 1)) Cos[t]^2) Sin[t], (e u^p + (1 + (d - b u) (u - 1)) Cos[t]^2) Cos[t], 2 u}, {t, -0.2, Pi + 0.2}, {u, 0, 1.1}, Lighting -> "Neutral", Mesh -> None, PlotStyle -> Directive[Specularity[0], RGBColor[0.92, 0.85, 0.73]], Axes -> False], {{a, ...


28

Well, an unusual question to answer, what about something like this Plot3D[.7*(1 + Tanh[1 - (2*Y^2 + X^2 + X^4)]) - .3*Exp[-X^2/.0025]* Exp[-(Y - .1)^2/.15] - .2*(Exp[-(X - .7)^2/.02]*Exp[-(Y - .0)^2/.08] + Exp[-(X + .7)^2/.02]*Exp[-(Y - .0)^2/.08]), {X, -1, 1}, {Y, -1, 1}]


181

This might get me suspended from the site butt I cannot resist. The shape you are looking for can probably be approximated (depending how anal you want to be about the outcome) by two assymetric probability distributions. The obvious choices would be a Poasson or a log normal distribution. I will use the latter as it is continuous. Now the bummer is that ...



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