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2

Update: A more convenient way than the original post is to generate a seperate graph with only the new edges and combine the GraphicsGroupBoxes of the two graphs: ClearAll[graphAddF] graphAddF = RawBoxes[With[{gg2 = Cases[ToBoxes[#2], GraphicsGroupBox[x_] :> x[[1]], {0, Infinity}][[1]]}, Replace[ToBoxes[#], GraphicsGroupBox[{x_, y_}] :> ...


3

I like the existing answers but I cannot resist posting my own formulation. I shall make use of the new-in-10.1 CirclePoints though I shall also provide an alternative without it. First Rules that specify the thickness of each radial line, counterclockwise from 3 o'clock: rls = {"a1" -> {3, 3, 3, 3}, "b1" -> {1, 3, 1, 3}, "c1" -> {1, 1, 3, 3}, ...


5

You could define $a_1,a_2$,.. as graphic primitives (Line) and use Translate: a1 = {Thickness[.01], Line[{{{0, 0}, {1, 0}}, {{1/2, -1/2}, {1/2, 1/2}}}]}; a2 = {Line[{{{0, 0}, {1, 0}}, {{1/2, -1/2}, {1/2, 1/2}}}]}; b1 = {Line[{{0, 0}, {1, 0}}], Thickness[.01], Line[{{1/2, -1/2}, {1/2, 1/2}}]}; b2 = {Line[{{1/2, -1/2}, {1/2, 1/2}}], Thickness[.01], Line[{{0, ...


4

One way of doing that is create an image for each element and then use GraphicsGrid With the definition about line of @halmir a1 = {Thickness[.03], Line[{{{0, 0}, {1, 0}}, {{1/2, -1/2}, {1/2, 1/2}}}]}; a2 = {Line[{{{0, 0}, {1, 0}}, {{1/2, -1/2}, {1/2, 1/2}}}]}; b1 = {Line[{{0, 0}, {1, 0}}], Thickness[.03], Line[{{1/2, -1/2}, {1/2, 1/2}}]}; b2 = ...


4

Sjoerd's approach using SARIMAProcess and TimeSeriesModelFit, in particular the last portion with in which you test SARIMA models of differing orders and observe which models are particularly favoured by the AIC, is certainly a valid approach. However, since you asked about periodograms and other spectral methods in your question, I thought I'd give an ...


4

First, paste your two columns of data copied from Google docs in Mathematica: data = ImportString["Day\tTraffic 1/12/2014\t3 2/12/2014\t15 . . . 5/5/2015\t109 6/5/2015\t282", "TSV"] // Rest; Then convert the few 14s and 15s mingled between the 2014s and 2015s to full years, and convert to a TimeSeries: dataTS = MapAt[ ...


2

Since you did not include your data, I am generating some fake data to play with: fakedata = Transpose@ Insert[ Transpose@ Insert[ RandomReal[{0, 1}, {6, 50}], Array["y lbl " <> ToString@# &, 50], 1 ], {""}~Join~Array["x lbl " <> ToString@# &, 6], 1 ]; The code first generates a 6-row by ...



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