Open alexruiz95 opened 4 years ago
@alexruiz95 I suspect Matlab won't be too easy to get into this structure. I'll need to think how to export it in a better way. maybe savemat
or just creating ASCII files.
@alexlib @alexruiz95 can you try to load this mat file? https://www.dropbox.com/s/zyxbiz884a1sayo/test.mat?dl=0
I was able to load the mat file. It resulted in two workspace variables t 1x121 single and x 121x8000x3 single
I'm assuming there are 121-time steps. How many trajectories should there be ?
8000
I was able to load the mat file. It resulted in two workspace variables t 1x121 single and x 121x8000x3 single
I'm assuming there are 121-time steps. RIGHT How many trajectories should there be ? 8000
Okay, I should be able to get this work. Thanks!
What should the flow field look like? I just plotted the trajectories and it looks a little weird. Let me know if this correct, if not ill fix my code and try again(:
I think it's right - we took a homogeneous isotropic case and just throw particles randomly in groups every group starts from some position and spreads. it's great to learn the PTV (not so much the flow field) and the effect of retracting and two-point statistics.
Great. I am currently working out the image generation aspect. I may isolate one of the groups to study since the domain is spread out and rather large. I will admit I am unfamiliar with the "effect of retracting and two-point statistics" aspect, so any literature/knowledge/codes on that subject would be greatly appreciated!
sorry, it's a typo: = re-tracking. I meant that you will create now images from these trajectories and then we'll track them and compare.
two-point means that we want to see how distance between two particles is affected by the aberrations and imperfect calibration
OH okay! Would it be okay to isolate one of the groups? Essentially zoom in / scale it up to enhance the images? Right now the pixels mesh together. this is only 2000 trajectories.
of course. you can also subsample it anyway you want
The images I generated weren't the best. But tracking a small region of 100 trajectories resulted in this so far:
Images:
Ground truth:
Reconstruction:
I will likely need to adjust the camera positions and tracking parameters to help optimize the PTV process. But this is a start! Any feedback would be greatly appreciated
Perhaps we can skype and devise a systematic approach for running these trials. Let me know
I will likely need to adjust the camera positions and tracking parameters to help optimize the PTV process. But this is a start! Any feedback would be greatly appreciated
it seems to be a great start. let's make a trial test (from the beginning to the end) and develop some figures and we'll know what is to improve for the next round. skype is a good idea
Sounds good! Are you available for a skype chat tomorrow?
Sounds good! Are you available for a skype chat tomorrow?
yes, 9pm IL time sounds reasonable if it works for you
Could we do 12:00 PM EST US (Florida) which would be 7:00 PM your time?
Could we do 12:00 PM EST US (Florida) which would be 7:00 PM your time?
not tomorrow. too early. another day? Thursday ?
Yeah, I can do Thursday. I prefer to meet between 10am-5pm. I'll try my best to accommodate. We can always do 2:00 PM EST US (Florida) which would be 9:00 PM your time if that's better(:
Thursday 2pm EST US = 9pm IL is fine @alexruiz95
Sounds good TY!
Alex, Can you confirm that the JHU database that you are using belongs to homogenous turbulence case? Samik
I confirm. this is mentioned here https://journals.aps.org/prl/supplemental/10.1103/PhysRevLett.120.244502/SM.pdf
How do read the positions of the trajectories?
I can read the files in the folder with:
import h5py f1 = h5py.File('jhu_t4_n150')
import numpy as np data = np.load('trajectories.npz')
Don't know how to access the elements/attributes