I have to create octree in parallel on Pyspark dataframe. and I have to use create octree with bottom up approach. i am not able to clearly understand bottom up approach. Can someone please write briefly direction? what I need to start. First I divided all points into 8 chunks.
Help me
Have you tried spark3D? Information on Octree partitioning in Python is here.
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Iam doing data mining and i dont know if going to use Table or Pandas?
any information for select the most suitable library for manage my dataset going to be welcome. Thank for any answer that help me in this.
I am an Orange programmer, and I'd say that if you are writing python scripts to analyze data, start with numpy + sklearn or Pandas.
To create an Orange.data.Table, you need to define Domain, which Orange uses for data transformations. Thus, tables in Orange are harder to create (but can, for example, provide automatic processing of testing data).
Of course, if you need to interface something specific from Orange, you will have to make a Table.
I am trying to extract rotation matrix and translation matrix from essential matrix.
I took these answers as reference:
Correct way to extract Translation from Essential Matrix through SVD
Extract Translation and Rotation from Fundamental Matrix
Now I've done the above steps applying SVD to essential matrix, but here comes the problem. According to my understanding about this subject, both R and T has two answers, which leads to 4 possible solutions of [R|T]. However only one of the solutions would fit in the physical situation.
My question is how can I determine which one of the 4 solutions is the correct one?
I am just a beginner on studying camera position. So if possible, please make the answer be as clear (but simple) as possible. Any suggestion would be appreciated, thanks.
The simplest is testing a point 3D position using the possible solution, that is, a reconstructed point will be in front of both cameras in only one of the possible 4 solutions.
So assuming one camera matrix is P = [I|0], you have 4 options for the other camera, but only one of the pairs will place such point in front them.
More details in Hartley and Zisserman's multiple view geometry (page 259)
If you can use Opencv (version 3.0+), you count with a function called "recoverPose", this function will do that job for you.
Ref: OpenCV documentation, http://docs.opencv.org/trunk/modules/calib3d/doc/calib3d.html
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 have a seemingly simple problem, but an easy solution is alluding me. I have a very large series (tens or hundreds of thousands of points), and I just need to visualize it at different zoom levels, but generally zoomed well out. Basically, I want to plot it in a tool like Matlab or Pyplot, but knowing that each pixel can't represent the potentially many hundreds of points that map to it, I'd like to see both the min and the max of all the array entries that map to a pixel, so that I can generally understand what's going on. Is there a simple way of doing this?
Try hexbin. By setting the reduce_C_function I think you can get what you want. Ex:
import matplotlib.pyplot as plt
import numpy as np
plt.hexbin(x,y,C=C, reduce_C_function=np.max) # C = f(x,y)
would give you a hexagonal heatmap where the color in the pixel is the maximum value in the bin.
If you only want to bin in one direction, see this this method.
First option you may want to try is Gephi- https://gephi.org/
Here is another option, though I'm not quite sure it will work. It's hard to say without seeing the data.
Try going to this link- http://bl.ocks.org/3887118. Do you see toward the bottom of the page data.tsv with all of the values? IF you can save your data to resemble this then the HTML code above should be able to build your data in the scatter plot example shown in that link.
Otherwise, try visiting this link to fashion your data to a more appropriate web page.
There are a set of research tools called TimeSearcher 1--3 that provide some examples of how to deal with large time-series datasets. Below are some example images from TimeSearcher 2 and 3.
I realized that simple plot() in MATLAB actually gives me more or less what I want. When zoomed out, it renders all of the datapoints that map to a pixel column as vertical line segments from the minimum to the maximum within the set, so as not to obscure the function's actual behavior. I used area() to increase the contrast.
I have a program which outputs to the terminal a number, one line at a time.
My goal is to have something else read these numbers and graph them in a line plot in real time. matplotlib and wxpython have been suggested, but I'm not sure how to go about implementing these.
See the following links:
What is the best real time plotting widget for wxPython?
Minimalistic Real-Time Plotting in Python
http://eli.thegreenplace.net/2008/08/01/matplotlib-with-wxpython-guis/
http://wxpython-users.1045709.n5.nabble.com/real-time-data-plots-td2344816.html
As some of those point out, you might be able to use wx's PyPlot for something really simple or use Chaco.
I really like this library for HTML5 graphing. Here is demo of real time updates: http://dygraphs.com/gallery/#g/dynamic-update
Are you simply asking for recommendations on plotting libs?