| 11 Mar 2020 |
|  tjark changed the history visibility to "world_readable" from "shared". | 19:11:20 |
| 12 Mar 2020 |
 @jonpsy:matrix.org | Hey hi | 01:09:32 |
 shlydv | Hi guys, I contacted earlier for working on CTLearn for GSoC 2020. I had a doubt regarding submitting a pull request before the actual proposal. Do we solve random issues on the repository or start working on a proof-of-concept framework for the ROOT input project mentioned in the ideas page? | 10:06:03 |
| @jonpsy:matrix.org | The former I believe | 10:08:25 |
| @jonpsy:matrix.org left the room. | 10:08:38 |
| shlydv | Ah cool | 10:09:29 |
 Tjark | Hi guys! Thanks for your interest In choosing CTLearn for GSoC 2020. First step would be to get familiar with the DL1-datahandler, especially with the writer.py. I will point you to some open dummy data from CTA and MAGIC later. | 10:21:05 |
| Tjark | https://github.com/cta-observatory/ctapipe-extra/blob/master/ctapipe_resources/gamma_test.simtel.gz | 10:23:51 |
| Tjark | There are also two MAGIC files in the ctapipe_resources folder. I think they actually contains real crab data. | 10:25:29 |
| shlydv | Thanks a lot for the resources! Took me quite some time to find the datasets on my own 🙂. | 10:58:04 |
| 13 Mar 2020 |
|  @slal:matrix.org joined the room. | 03:59:40 |
| 14 Mar 2020 |
|  ayushvpaliwal joined the room. | 08:37:18 |
| 18 Mar 2020 |
| Tjark | Hi @all!
I hope the DL1 data handler tool didn't cause any troubles 😄. Did you guys managed to use this tool to produce a hdf5 file with the dummy data? With this file you should be able to launch CTLearn for some test runs.
Further steps would be to read in trees from the MAGIC root file https://github.com/cta-observatory/ctapipe-extra/blob/master/ctapipe_resources/20131004_M1_05029747.003_Y_MagicCrab-W0.40%2B035.root using the uproot tool https://github.com/scikit-hep/uprootÂ
The tree that contains the pixel charges is ' Events.MCerPhotEvt.fPixels.fPixels.fPhot’. A great exercise would be to read in some random events and convert the 1D array (hexagonal grid) to a 2d square images using the image mapper in dl1 data handler. You need to select the ‘MAGICCam’ as the camera type.
In case you need more literature:
https://arxiv.org/abs/2001.03602
https://arxiv.org/abs/1912.09898
https://arxiv.org/abs/1912.09877
https://arxiv.org/abs/1709.05889
https://www.cta-observatory.org
https://veritas.sao.arizona.edu/
https://magic.mpp.mpg.de/
https://www.mpi-hd.mpg.de/hfm/HESS/ | 21:00:36 |
| Tjark | (edited) ... in trees ... => ... in a couple of trees ... | 21:02:50 |
| 21 Mar 2020 |
|  ganeshtero joined the room. | 12:31:07 |
| 23 Mar 2020 |
| ayushvpaliwal | Yes, sir, I got quite familiar with dl1DH. And was able to produce a HDF5 file from dummy data. The given exercise was interesting. I made the required conversion. I have further doubt on same. Sir, there we are using five different methods of conversions. Can we train some data after applying these processing on any neural network model to determine which is best of them all? Or their use is event dependent? | 18:02:36 |
| 24 Mar 2020 |
| Tjark | Awesome! What is stored in the hdf5 are the calibrated charges of the PMTs in the camera. In most of the IACT cameras this layout is a hexagonal grid. To plug our data to CNNs we need to interpolation our events to square the images (see https://arxiv.org/abs/1912.09898). The image_mapper is performing this task. For now, you can just use the bilinear interpolation as the mapping method. With this dummy data you should be able to start training the CTLearn models, but the results are meaningless, because it's just a handful gamma events. However, it's a good exercise to get familiar with CTLearn. A more important exercise would be to read in some MAGIC events from the root file using the uproot tool and then plot the squared images of these events. Feel free to share results (event images) at earlier stage 🙂 | 10:32:19 |
