Skip to main content

graphics - AspectRatio option works unpredictably?


Let us examine the agreement between specified value for the AspectRatio option and the actual aspect ratio of generated plot. For this purpose I define a function which uses the Rasterize trick for determination of the actual aspect ratio:



realAspectRatio = #2/#1 & @@ (#2 - #1) & @@ 
Rasterize[
Show[#, Epilog -> {Annotation[
Rectangle[ImageScaled[{0, 0}], ImageScaled[{1, 1}]], "Two",
"Region"]}], "Regions"][[-1, 2]] &;

(Instead of realAspectRatio@ one can use much more basic but lesser precise function #2/#1 & @@ ImageDimensions@ what gives the same results.)


At first, we check the agreement for simple graphics Graphics[{Point[{{0, 0}, {1, 1}}]}]:


table = Table[{10^p, realAspectRatio@
Graphics[{Point[{{0, 0}, {1, 1}}]}, AspectRatio -> 10^p]}, {p, -2, 2, .1}];

lm = LinearModelFit[table, x, x];
lm["BestFit"]
ListPlot[lm["FitResiduals"], Frame -> True,
FrameLabel -> {"AspectRatio", "fit residual"}]


1.26421 + 0.603143 x


plot



One can see that actual aspect ratio substantially differs from the requested via the AspectRatio option and the dependence between them is nonlinear!



In this case the situation becomes MUCH better if we provide at least horizontal PlotRange specification PlotRange -> {{0, 1}, Automatic}:


table = Table[{10^p, 
realAspectRatio@
Graphics[{Point[{{0, 0}, {1, 10}}]}, AspectRatio -> 10^p,
PlotRange -> {{0, 1}, Automatic}]}, {p, -2, 2, .1}];
lm = LinearModelFit[table, x, x];
lm["BestFit"]
ListPlot[lm["FitResiduals"], Frame -> True,
FrameLabel -> {"AspectRatio", "fit residual"}]



0.00391481 + 1.00225 x


plot



But for a little more complex graphics ListPlot[Prime[Range[25]]] this remedy does not help even if we vary AspectRatio in much lesser diapason from 1/5 to 5:


table = Table[{ar, realAspectRatio@
ListPlot[Prime[Range[25]], PlotRange -> {{0, 25}, {0, 100}},
AspectRatio -> ar]}, {ar, 1/5, 5, .1}];
lm = LinearModelFit[table, x, x];
lm["BestFit"]

ListPlot[lm["FitResiduals"], Frame -> True,
FrameLabel -> {"AspectRatio", "fit residual"}]


0.248369 + 0.768459 x


plot



How this behavior can be explained? Is it intended behavior? And more importantly: is there a way to make the actual aspect ratio predictable?




Summary of the discussion and the conclusion



The source of confusion was the first sentence under the "Details" section on the Documentation page for AspectRatio: "AspectRatio determines the scaling for the final image shape." This statement is clearly wrong and more correct statement can be found as the first point under the "Properties and Relations" subsection in the "Examples" section on the same page: "AspectRatio determines the ratio of PlotRange, not ImageSize." But as it is clearly demonstrated in dedicated answer, even this statement is not precise because actually AspectRatio determines the aspect ratio of the plotting area which is specified by both PlotRange and PlotRangePadding.



Answer



It seems to me that there is little point in making the image size so small that the image cannot possibly have a close to accurate aspect ratio. If I also get rid of the superfluous parts outside of the plot range by setting PlotRangePadding, ImagePadding, and ImageMargins all to zero, I can use simple ImageDimensions to check aspect ratio. Doing all this I find that it is quite precise in 10.1 under Windows:


table = Table[{10^p, 
N[#2/#] & @@
ImageDimensions@
Image@Graphics[{Disk[]}, AspectRatio -> 10^p, PlotRangePadding -> 0,
ImagePadding -> 0, ImageMargins -> 0, ImageSize -> {2000}]}, {p, -2, 2, .1}];

lm = LinearModelFit[table, x, x];

lm["BestFit"]
ListPlot[lm["FitResiduals"], Frame -> True, FrameLabel -> {"AspectRatio", "fit residual"},
DataRange -> {10^-2, 10^2}, PlotRange -> All]


-0.00279057 + 1.00046 x

enter image description here


Likewise your ListPlot example:


table = Table[{ar, 

N[#2/#] & @@
ImageDimensions@
Image@ListPlot[Prime[Range[25]], PlotRange -> {{0, 25}, {0, 100}},
AspectRatio -> ar, PlotRangePadding -> 0, ImagePadding -> 0, ImageMargins -> 0,
ImageSize -> {2000}]}, {ar, 1/5, 5, .1}];

lm = LinearModelFit[table, x, x];
lm["BestFit"]
ListPlot[lm["FitResiduals"], Frame -> True, FrameLabel -> {"AspectRatio", "fit residual"},
DataRange -> {1/5, 5}]



0.000344083 + 0.999798 x

enter image description here


Comments

Popular posts from this blog

plotting - Filling between two spheres in SphericalPlot3D

Manipulate[ SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, Mesh -> None, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], {n, 0, 1}] I cant' seem to be able to make a filling between two spheres. I've already tried the obvious Filling -> {1 -> {2}} but Mathematica doesn't seem to like that option. Is there any easy way around this or ... Answer There is no built-in filling in SphericalPlot3D . One option is to use ParametricPlot3D to draw the surfaces between the two shells: Manipulate[ Show[SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], ParametricPlot3D[{ r {Sin[t] Cos[1.5 Pi], Sin[t] Sin[1.5 Pi], Cos[t]}, r {Sin[t] Cos[0 Pi], Sin[t] Sin[0 Pi], Cos[t]}}, {r, 1, 2 - n}, {t, 0, Pi}, PlotStyle -> Yellow, Mesh -> {2, 15}]], {n, 0, 1}]

plotting - Plot 4D data with color as 4th dimension

I have a list of 4D data (x position, y position, amplitude, wavelength). I want to plot x, y, and amplitude on a 3D plot and have the color of the points correspond to the wavelength. I have seen many examples using functions to define color but my wavelength cannot be expressed by an analytic function. Is there a simple way to do this? Answer Here a another possible way to visualize 4D data: data = Flatten[Table[{x, y, x^2 + y^2, Sin[x - y]}, {x, -Pi, Pi,Pi/10}, {y,-Pi,Pi, Pi/10}], 1]; You can use the function Point along with VertexColors . Now the points are places using the first three elements and the color is determined by the fourth. In this case I used Hue, but you can use whatever you prefer. Graphics3D[ Point[data[[All, 1 ;; 3]], VertexColors -> Hue /@ data[[All, 4]]], Axes -> True, BoxRatios -> {1, 1, 1/GoldenRatio}]

plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1.