My goal is to achieve something that was previously asked in this site (outside from SO). In this external site the questions is unanswered, and in order to give more visibility and to try to get an answer I translate it to here:
The issue is:
I have a small simulation of particles flowing through a wire mesh structure, and I'm interested in calculating the mass flow rate and volume fraction of particles at certain cross sections. I think I understand how to calculate mass flow rate by setting up small regions and dumping particle count and velocity from that region. I assume that volume fraction works in a similar fashion, except I only need to know the size of my particles and my dump region.
What I'm wondering is this - is it possible to do these things in Paraview? I can set up planes and slices and such, but I can't seem to extract much useful information out of them.
Further on down the road, what I would like to do would be to plot contours of volume fraction at certain planes, and plot the volume fraction along the vertical axis so I can see how high the particles are piling up on top of the screen, based on particle size, wire size, etc. Can Paraview do any of this?
This is a visualization issue. I don't know how make it with Paraview. The idea is count how much particles cross the slice.
My first approach was piped: DataReader | Spherical Glyph | Slice with normal fixed handly along z axis but nothing results. Also I tried to adding the filter Surface Flow and nothing too. Probably I am piping the data in a bad way.
To see the pipelining process I add an image (focus in PlotOverLine1 and its above pipes):
Related
I'm building a system that receives a stream of live GPS coordinates, around every second or two. I would like to smooth out the visualisation of it so that the location doesn't jump too much when there is a less then accurate datapoint. I would also like to visualise the speed of the moving point, not just jump from one to another.
To do it I have implemented a Kalman filter that gets the coordinates and models speed and acceleration. This helps smooth out the curve but the output of a Kalman filter has the same rate of one data point per second. I thought I would be able to interpolate this by running the "predict" part of Kalman filter 60 times per second, while the update part would only be done when a new coordinate arrives.
However, it turns out this results in a non continuous function and there is a visual jump after a new update comes in.
How can I solve this problem? Is there an algorithm that would output a continuous smooth path while feeding it coordinate points?
Im trying to transform a path along an arc.
My project is running on osX 10.8.2 and the painting is done via CoreAnimation in CALayers.
There is a waveform in my project which will be painted by a path. There are about 200 sample points which are mirrored to the bottom side. These are painted 60 times per second and updated to a song postion.
Please ignore the white line, it is just a rotation indicator.
What i am trying to achieve is drawing a waveform along an arc. "Up" should point to the middle. It does not need to go all the way around. The waveform should be painted along the green circle. Please take a look at the sketch provided below.
Im not sure how to achieve this in a performant manner. There are many points per second that need coordinate correction.
I tried coming up with some ideas of my own:
1) There is the possibility to add linear transformations to paths, which, i think, will not help me here. The only thing i can think of is adding a point, rotating the path with a transformation, adding another point, rotating and so on. But this would be very slow i think
2) Drawing the path into an image and bending it would surely lead to image-artifacts.
3) Maybe the best idea would be to precompute sample points on an arc, then save save a vector to the center. Taking the y-coordinates of the waveform, placing them on the sample points and moving them along the vector to the center.
But maybe i am just not seeing some kind of easy solution to this problem. Help is really appreciated and fresh ideas very welcome. Thank you in advance!
IMHO, the most efficient way to go (in terms of CPU usage) would be to use some form of pre-computed approach that would take into account the resolution of the display.
Cleverly precomputed values
I would go for the mathematical transformation (from linear to polar) and combine two facts:
There is no need to perform expansive mathematical computation
There is no need to render two points that are too close from each other
I have no ready-made algorithm for you, but you could use a pre-computed sin or cos table, and match the data range to the display size in order to work with integers.
For instance imagine we have some data ranging from 0 to 1E6 and we need to display the sin value of each point in a 100 pix height rectangle. We can use a pre-computed sin table and work with integers. This way displaying the sin value of a point would be much quicker. This concept can be refined to get a nicer result.
Also, there are some ways to retain only significant points of a curve so that the displayed curve actually looks like the original (see the Ramer–Douglas–Peucker algorithm on wikipedia). But I found it to be inefficient for quickly displaying ever-changing data.
Using multicore rendering
You could compute different areas of the curve using multiple cores (can be tricky)
Or you could use pre-computing using several cores, and one core to do finish the job.
I'm considering exploiting ray coherence in my software per-pixel realtime raycaster.
AFAICT, using a uniform grid, if I assign ray coherence to patches of say 4x4 pixels (where at present I have one raycast per pixel), given 16 parallel rays with different start (and end) point, how does this work out to a coherent scene? What I foresee is:
There is a distance within which the ray march would be exactly the same for adjacent/similar rays. Within that distance, I am saving on processing. (How do I know what that distance is?)
I will end up with a slightly to seriously incorrect image, due to the fact that some rays didn't diverge at the right times.
Given that my rays are cast from a single point rather than a plane, I guess I will need some sort of splitting function according to distance traversed, such that the set of all rays forms a tree as it move outward. My concern here is that finer detail will be lost when closer to the viewer.
I guess I'm just not grasping how this is meant to be used.
If done correctly, ray coherence shouldn't affect the final image. Because the rays are very close together, there's a good change that they'll all take similar paths when traversing the acceleration structure (kd-tree, aabb tree, etc). You have to go down each branch that any of the rays could hit, but hopefully this doesn't increase the number of branches much, and it saves on memory access.
The other advantage is that you can use SIMD (e.g. SSE) to accelerate some of your tests, both in the acceleration structure and against the triangles.
We're building a GIS interface to display GPS track data, e.g. imagine the raw data set from a guy wandering around a neighborhood on a bike for an hour. A set of data like this with perhaps a new point recorded every 5 seconds, will be large and displaying it in a browser or a handheld device will be challenging. Also, displaying every single point is usually not necessary since a user can't visually resolve that much data anyway.
So for performance reasons we are looking for algorithms that are good at 'reducing' data like this so that the number of points being displayed is reduced significantly but in such a way that it doesn't risk data mis-interpretation. For example, if our fictional bike rider stops for a drink, we certainly don't want to draw 100 lat/lon points in a cluster around the 7-Eleven.
We are aware of clustering, which is good for when looking at a bunch of disconnected points, however what we need is something that applies to tracks as described above. Thanks.
A more scientific and perhaps more math heavy solution is to use the Ramer-Douglas-Peucker algorithm to generalize your path. I used it when I studied for my Master of Surveying so it's a proven thing. :-)
Giving your path and the minimum angle you can tolerate in your path, it simplifies the path by reducing the number of points.
Typically the best way of doing that is:
Determine the minimum number of screen pixels you want between GPS points displayed.
Determine the distance represented by each pixel in the current zoom level.
Multiply answer 1 by answer 2 to get the minimum distance between coordinates you want to display.
starting from the first coordinate in the journey path, read each next coordinate until you've reached the required minimum distance from the current point. Repeat.
Recently I've started developing voxel engine. What I need is only colorful voxels without texture, but at very large amount (much smaller than minecraft) - and the question is how to draw the scene very fast? I'm using c#/xna but this is in my opinion not very important in this case, let's talk about general cases. Look at these two games:
http://www.youtube.com/watch?v=EKdRri5jSMs
http://www.youtube.com/watch?v=in0bavLJ8KQ
Especially I think video number 2 represents great optimization methods (my gfx card starts choking just at 192 x 192 x 64) How they achieve this?
What i would to have in the engine:
colorful voxels without texture, but shaded
many, many voxels, say minimum 512 x 512 x 128 to achieve something like video #2
shadows (smooth shadows will be great but this is not necessary)
optional: dynamic lighting (for example from fireballs flying, which light up near voxel structures)
framerate minimum 40 FPS
camera have 3 ways of freedom (move in x-axis, move in y-axis, move in z-axis), no camera rotation is needed
finally optional feature may be Depth of Field (it will be sweet ^^ )
What optimization I have already know:
remove unseen voxels that resides inside voxel structure (covered
from six directions by other voxels)
remove unseen faces of voxels - because camera have no rotation and always look aslant forward like in TPP games, so if we divide screen
by vertical cut, left voxels and right voxels will show only 3 faces
keep voxels in Dictionary instead of 3-dimensional array - jumping through array of size 512 x 512 x 128 takes miliseconds which is
unacceptable - but dictionary int:color where int describes packed
3D position is much much faster
use instancing where applciable
occluding? (how to do this?)
space dividing / octtree (is it good idea?)
I'll be very thankful if someone give me a tip how to improve existing optimizations listed above or can share ideas of new improvements. Thanks
1) Voxatron uses a software renderer rather than the GPU. You can read some details about it if you read the comments in this blog post:
http://www.lexaloffle.com/bbs/?tid=201
I haven't looked in detail myself so can't tell you much more than that.
2) I've never played 3D Dot Game Heroes but I don't have any reason to believe it uses voxels at all. I mean, I don't see any cubes being added or deleted. Most likely it is just a static polygon mesh with a nice texture applied.
As for implementing it yourself, do not try to draw the world by rendering cubes as this is very slow. Instead you should process the volume and generate meshes lying on the intersection of solid voxels and empty ones. Break the volume into suitable sized regions (e.g. 32x32x32) and generate a mesh for each.
I have written a book article about this which you might find useful. It's actually about smooth voxel terain but a lot of the priciples stll apply.
You can read it on Google books here: http://books.google.com/books?id=WNfD2u8nIlIC&lpg=PR1&dq=game%20engine%20gems&pg=PA39#v=onepage&q&f=false
And you can find the associated source code here: http://www.thermite3d.org
Since you are using XNA, you can just use instancing to get the desired effect: http://www.float4x4.net/index.php/2010/06/hardware-instancing-in-xna/
http://roecode.wordpress.com/2008/03/17/xna-framework-gameengine-development-part-19-hardware-instancing-pc-only/
The underlying concept is instancing: this feature lets you specify some amount of repeating data and some amount of varying data in a single DrawIndexedPrimitive call. In your case, the instance stream would be a single solid box, and the other stream would be the transform and color information.