I'm looking for an algorithm or method, that can visualize/create nice diagram from data. The data has size and quality properties (and there are some categorys). I found out that treemaps/heatmaps are good for that. But I need an extreme solution. Something like a cell.
I have a good example:
https://www.destatis.de/Voronoi/PriceKaleidoscope.svg
Is it possible to generate diagrams like that (with changing data)? Is there an algorithm for that? Where should I look for more information? Is there a program that does something similar to that?
What you want is called a vornonoi treemap. Check out the links on this other stackoverflow post and this paper from SoftVis 2005.
You can make that kind of map via d3.js http://d3js.org/ ,and there is a treemap example http://mbostock.github.com/d3/talk/20111018/treemap.html .
Related
I am reading the tutorials from eeglab and I would like to know which polarities are used for the computation of component ERPs. What it says in the tutorial is:
link so
I want to know because I have realized that when I am plotting individual datatests I have differences in the scalp map polarities even if in my cluster the scalp maps look the same.
Thank you in advance.
Eva
I am simulating the case "Cavity driven lid" and I try to get all the stream lines with the stream tracer of paraview, but I only get the ones that intersect the reference line, and because of that there are vortices that are not visible. How can I see all the stream-lines in the domain?
Thanks a lot in adavance.
To add a little bit to Mathieu's answer, if you really want streamlines everywhere, then you can create a Stream Tracer With Custom Source (as Mathieu suggested) and set your data to both the Input and the Seed Source. That will create a streamline originating from every point in your dataset, which is pretty much what you asked for.
However, while you can do this, you will probably not be happy with the results. First of all, unless your data is trivially small, this will take a long time to compute and create a large amount of data. Even worse, the result will be so dense that you won't be able to see anything. You will get all those interesting streamlines through vortices, but they will be completely hidden by all the boring streamlines around them.
Thus, you are better off with trying to derive a data set that contains seed points that are likely to trace a stream through the vortices that you are interested in. One thing you might want to try is to compute the vorticity of your vector field (Gradient Of Unstructured Data Set when turning on advanced option Compute Vorticity), find the magnitude of that (Calculator), and then use the Threshold filter to pull out the cells with large vorticity. Then use that as your Seed Source.
Another (probably better) option if your data is 2D or you can extract an interesting surface along the flow of your data is to use the Surface LIC plugin. Details can be found at https://www.paraview.org/Wiki/ParaView/Line_Integral_Convolution.
You have to choose a representative source for your streamline.
You could use a "Sphere Source", so in the StreamTracer properties.
If that fails, you can use a StreamTracerWithCustomSource and use your own source that you will have to create yourself first.
I have been researching on how to program image processing for counting objects and I found the following homepage about Matlab for cell counting
I am not familiar with Matlab, but found their ideas interesting, so reading this, I see that they are using a BlobAnalysis Object to find the centroid of the segmented cells (You can see the image :
Now, this seem very interesting but I wonder what this operation is doing exactly? (please don't give me the definition written in the docs-"it is finding the property in the blobs". How? is it segmenting it? separating it? ) As far as I can see either you separate the cells first (that is the whole point of counting- I am doing this in other program using watersheding) and then finding the centroids is just something added and not so important, OR somehow this blob analysis is doing some interesting segmentation itself that I would like to know.
Anyone familiar with this can give me some pointers or advice here?
I thought this should be a simple task, I just can't find the way to do it:
I am using 'imregister' (MATLAB) to register two medical X-ray images.
To ensure I get the best registration outcome as possible, I use some image processing techniques such as contrast enhancement, blackening of objects that are different between images and even cropping.
The outcome of this seems to be quite satisfying.
Now, I want to perform the exact same registaration on the original images, so that I can display the two ORIGINAL images automatically in alignment.
I think that an output parameter such as tform serves this purpose of performing a certain registration on any two images, but unfortunately 'imregister' does not provide such a parameter, as far as I know.
It does provide as an output the spatial referencing object R_reg which might be the answer, but I still haven't figured out how to use it to re-preform the registration.
I should mention that since I am dealing with medical X-ray images on which non of the feature-detecting algorithms seem to work well enough to perform registration, I can only use intensity-based (as opposed to feature-based) registration, and therefore am using 'imregister'.
Does anyone know how I can accomplish this?
Thanks!
Noga
So to make an answer out of my comment, there are 2 things you can do depending on the Matlab release you are using:
Option 1: R2013a and earlier
I suggest modifying the built-in imregister function by forcing tform to be an output and save that function under another name.
For example:
function [movingReg,Rreg,tform] = imregister2(varargin)
save that, add it to your path and you're good to go. If you type edit imregister you will notice that the 1st line calls imregtform to get the geometric transformation required, while the last line calls imwarp, to apply that geometric transformation. Which leads us to Option 2.
Option 2: R2013b and later
Well in that case you can directly use imregtform to get the tform object and then use imwarpto apply it. Easy isn't it?
Hope that makes things clearer!
I am new to WinBUGS and OpenBUGS. I just ran an example model, and am wondering whether I can get the predictions generated by WinBUGS/OpenBUGS. If not, is there any convenient way to achieve this (e.g. with the help of other applications such as R)?
Yes, you can. In Bayesian tools, it is very easy to get predictions. In your design matrix, just add new rows, with response variable set to NA. You can see concrete example here.