When drawing curved shapes in R, they are typically made up of a collection of short straight line segments (with circular line endings). The result looks good to the human eye but is a bit of a roundabout way of doing it as you need to interpolate the curve to get the coordinates of the segments. It also produces unnecessarily large files, although this is not a big problem unless the plot contains an extreme amount of curves.
Is there a way to create curved vector shapes the proper way using paths made up of anchor and pivot points as in the figure below?
Short answer: "No".
Long answer: R doesn't know if your output device supports describing curves from formulae - for example PDF and PostScript can (I think) have arcs and curves, but R doesn't know if its drawing to one of those or to a bitmap device.
Hence when you want to draw an arc on an R plot, it can't do:
0 0 moveto
25 25 pi arcto
(or whatever the PostScript is) to describe an arc that will be infinitely smoothly zoomable. It has to render the arc into segments.
If you run the example in help(bezierGrob) to a PDF device and zoom in, you can see the segments (zoomed in to 500%).
Related
I am playing around with rgl and I have created a 3D rendering of the mouse brain, in which structures can be isolated and coloured separately.
The original data is a 3D array containing evenly spaced voxels.
Every voxel is coded with a structure ID.
Every structure is rendered separately as a mesh by marching cubes, and smoothed using Laplacian smoothing as implemented by Rvcg.
Some of these structures can be quite small, and it would make sense to look at them within the context of the whole brain structure.
One of the options is to create a low-threshold mesh of the whole set of voxels, so that only the outer surface of the brain is included in the mesh.
This surface can be smoothed and represented using a low alpha in rgl::shade3d colouring faces. This however seems to be quite taxing for the viewport as it slows down rotation etc especially when alpha levels are quite low.
I was wondering if there is any way to implement some sort of cel shading in rgl, e.g. outlining in solid colours the alpha hull of the 2D projection to the viewport in real time.
In case my description was not clear, here's a photoshopped example of what I'd need.
Ideally I would not render the gray transparent shell, only the outline.
Cel shading example
Does anybody know how to do that without getting deep into OpenGL?
Rendering transparent surfaces is slow because OpenGL requires the triangles making them up to be sorted from back to front. The sort order changes as you rotate, so you'll be doing a lot of sorting.
I can't think of any fast way to render the outline you want. One thing that might work given that you are starting from evenly spaced voxels is to render the outside surface using front="points", back="points", size = 1. Doing this with the ?surface3d example gives this fake transparency:
If that's not transparent enough, you might be able to improve it by getting rid of lighting (lit = FALSE), plotting in a colour close to the background (color = "gray90"), or some other thing like that. Doing both of those gives this:
You may also be able to cull your data so the surface has fewer vertices.
i was wondering if anyone of u here knows how to smooth a polygon in Maya? I've tried 2 methods which i found online. One of which is 'Vertice Averaging' and the other 'Smooth' which are both under the 'Mesh' option.
Vertice Averaging caused my polygons to have 'gaps' or 'holes' between the triangles, which i do not intend for that to happen.
While Smooth causes the polygon's face to have 4 vertex instead of the original 3, which i do not want as well, as i need a polygon with triangle faces.
http://img.photobucket.com/albums/v483/dragonlancer/PolygonAveragingampSmoothing.jpg
And to whoever told me that it is a bug, i tried, but doesnt work =[
You said you wanted to maintain your tris so you could switch the smooth option 'Add Divisions' from exponential to linear.
If you're getting gaps, its because the original mesh has verts which are not welded together. Try Edit Mesh > Merge with a small tolerance value before running average or smooth.
In general you'll get more pleasant results if you smooth a quadrangular mesh instead of a trimesh - when you subdivide quads the results are very similar to NURBS curves, whereas smoothed triangles always tend to look look like old-fashioned 1990's game graphics.
Using iGraph, how can I represent self-reflexive nodes with circle shaped curves? By default, these curves are represented by a pinched or tear drop shaped loop.
As Spacedman said, you would need to do quite some programming to do this. You could plot a graph without self-loops and then add them (graphs are basically a scatterplot and you can use points and similar functions to add lines to them), but this is not trivial (especially since you need to know the edge of nodes, not their center) and will cause the selfloops to be plotted on top of everything else which might not look good.
This weekend I have updated qgraph with how self-loops work. qgraph can be used to plot networks and should play nicely with igraph. e.g.:
# An adjacency matrix:
A <- matrix(1,3,3)
library("igraph")
# igraph graph and layout:
Graph <- graph.adjacency(A)
Layout <- layout.circle(Graph)
# Plot in qgraph:
library("qgraph")
qgraph(get.adjacency(Graph,sparse=FALSE),layout=Layout,diag=TRUE,directed=TRUE)
I am quite content with how these self-loops turned out and they seem to be more to what you describe. So this could be an option. However, my loops are just as hardcoded. For reference, I compute the edge of a node (starting and ending point of the loop) with the inner function qgraph:::Cent2Edge and compute the shape of the loop (spline) with the inner function qgraph:::SelfLoop.
Inside plot.igraph you can see that loops are drawn using a plot.bezier function, and all the control for that is pretty much hard coded there. You'd have to rewrite large chunks of plot.igraph to call a plot.circle function you'd have to write to do this.
Also, I'm guessing you don't want complete circles, but circle segments that start on the edge of the vertex symbol (the default blue circle with the vertex number in it) and end (possibly with an arrowhead) on another part of the edge of the vertex symbol? Or do you want circles that touch the symbol like the bezier teardrop loops do?
Either way, the answer seems to be 'no, not without doing some programming or submitting a feature request to the igraph guys'
I posted an earlier answer saying the layout functions were involved, but that's not true - the layout functions only position the vertices, and it is plot.igraph's job to draw the edges.
Mapping a point cloud onto a 3D "fabric" then flattening.
So I have a scientific dataset consisting of a point cloud in 3D, this point cloud comprises points on a surface that is curved. In order to perform quantitative analysis I however need to map these point clouds onto a surface I can then flatten. I thought about using mapping tools sort of like in the case of the 3d world being flattened onto a map, but not sure how to even begin as I have no experience in cartography and maybe I'm trying to solve an easy problem with the wrong tools.
Just to briefly describe the dataset: imagine entirely transparent curtains on the window with small dots on them, if I could use that dot pattern to fit the material the dots are on I could then "straighten" it and do meaningful analysis on the spread of the dots. I'm guessing the procedure would be to first manually fit the "sheet" onto the point cloud data by using contours or something along those lines then flattening the sheet thus putting the points into a 2d array. Ultimately I'll probably also reduce that into a 1D but I assume I need the intermediate 2D step as the length of the 2nd dimension is variable (i.e. one end of the sheet is shorter than the other but still corresponds to the same position in terms of contours) I'm using Matlab and Amira though I'm always happy to learn new tools!
Any advice or hints how to approach are much appreciated!
You can use a space filling curve to reduce the 3d complexity to a 1d complexity. I use a hilbert curve to index lat-lng pairs on a 2d map. You can do the same with a 3d space but it's easier to start with a simple curve for example a z morton order curve. Space filling curves are often used in mapping applications. A space filling curve also adds some proximity information and a new sort order to the 3d points.
You can try to build a surface that approximates your dataset, then unfold the surface with the points you want. Solid3dtech.com has the tool to unfold the surfaces with the curves or points.
I have a set of points like this (that I have clustered using R):
180.06576696, 192.64378568
180.11529253999998, 192.62311824
180.12106092, 191.78020965999997
180.15299478, 192.56909828000002
180.2260287, 192.55455869999997
These points are dispersed around a center point or centroid.
The problem is that the points are very close together and are, thus, difficult to see.
So, how do I move the points apart so that I can distinguish each point more clearly?
Thanks,
s
Maybe I'm overlooking some intricacy here, but...multiply by 10?
EDIT
Assuming the data you listed above are Cartesian (x,y) coordinate pairs, you can visualize them as a scatter plot using Google Charts. I've rounded your data to 3 decimal places, because Google Charts doesn't appear to handle higher precision than that.
I don't know the coordinates for your central point. In the above chart, I'm assuming it is somewhere nearby and not at (0,0). If it is at (0,0), then I imagine it will be difficult to visualize all of the data at once without some kind of "zoom-in" feature, scaling the data, or a very large screen.
slotishtype, without going into code, I think you first need to add in the following tweaking parameters to be used by the visualization code.
Given an x by y display box, fill the entire box, with input parameters [0.0 to 1.0]...
overlap: the allowance for points to be placed on top of each other
completeness: how important is it to display all of your data points
centroid_display: how important is it to see the centroid in the same output
These produce the dependent parameter
scale: the ratio between display distances to numerical distances
You will need code to
calculate the distance(s) to the centroid like you said,
and also the distances between data points, affecting the output based on the chosen input parameters.
I take inspiration from the fundamentals in the GraphViz dot manual. Look at the "Drawing Orientation, Size and Spacing" on p12.