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6

data = Table[ PDF[BinomialDistribution[50, p], k], {p, {0.3, 0.5, 0.8}}]; DiscretePlot[ Evaluate@data, {k, 1, 50}, PlotStyle -> { Directive[Black, Opacity[.5], AbsoluteDashing[{5, 5}]], Directive[Black, Opacity[.5], AbsoluteDashing[{10, 10}]], Directive[Black, Opacity[.5], AbsoluteDashing[{0, 0}]]}, PlotMarkers -> {Automatic,...


6

A series of n cosine functions at successively doubled frequencies can be phased so their signs produce a Gray Code (– for 0, + for 1). If these cosines are also scaled to successively halved amplitudes, they partition the plane over one major cycle into 2^n non-overlapping regions, just like the circles of a Venn diagram. These regions cover the angle axis ...


5

Her is a start: Clear[ψD]; ψD[j_, k_][x_, y_] := Sin[j Pi x] Sin[k Pi y] Clear[ψN]; ψN[j_, k_][x_, y_] := Cos[j Pi x] Cos[k Pi y] ContourPlot[ψD[1, 2][x, y], {x, y} ∈ Polygon[{{0, 0}, {1, 0}, {1, 1}, {0, 1}}], PlotPoints -> 100, AspectRatio -> Automatic, PlotRange -> All] ContourPlot[ψN[2, 2][x, y], {x, y} ∈ Polygon[{{0, 0}, {1, 0}, {1,...


4

ArrayPlot is much more than just a simple array like Grid: it represents a ranged 2D dataset, and its visualization can be finetuned by options like DataReversed and DataRange. These features make it quite complicated to reproduce the same layout and order with Grid. Here I offer AnnotatedArrayPlot which comes in handy when your dataset is more than just a ...


2

You can automate this, but obviously only if you have a predefined number of ticks for each image size. You can adjust the range of the ticks to cover all functions, out of bound ticks are not shown. k = { Small, Medium, Large}; l = {{Table[i, {i, 0, 2, 1}], Table[i, {i, 0, 4, 2}]}, Automatic, {Table[i, {i, 0, 2, 0.1}], Table[i, {i, 0, 4, 0.5}]}}; p1 := ...


2

I would do something like the following: ListDensityPlot[list, PlotLegends->True, ColorFunction->"Rainbow"]



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