![\"app_screenshot\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F389918%2Fe3384cd667c3509fae577a232f5869a6%2Fapp_screenshot.PNG?generation=1676232706715471&alt=media\")
Hi everyone!
\nInspired by the great work visualising the data I've put together an app to display the direction vector, the DOM positions and animate the interactions from the first training batch. You can try it out here.
\nNote it can take awhile to load (30 seconds) but is generally quite quick once loaded so you're rewarded for the patience :)
\nA quick example
\n
Auxilary flag example
\nThere is also an auxiliary flag filter, so we can also recreate event 1583 highlighted by @jirkaborovec in this discussion trying to recreate the auxiliary true/false image in the data description:
Auxiliary = False:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F389918%2F15cbdc544c88937d2e4574ba71e13e67%2Fauxiliary_false.PNG?generation=1676234520453702&alt=media\")
Auxiliary = True:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F389918%2F26484284b168830506ec9973a991fbb4%2Fauxiliary_true.PNG?generation=1676234538743197&alt=media\")
One last example with lots of interactions
\nFinally quite an interesting one is 138523 ![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F389918%2Fa7f865089c36fd4b8f51d74ba745adb0%2F138523.PNG?generation=1676234165884015&alt=media\")
Hopefully this helps someone explore the data in a bit more detail :)
\nHow was the app put together?
\nAll the code is on my github
\nDisclaimer: This is the first time I've deployed anything on google cloud run, so let me know if the app doesn't seem to working!!
", "messages": [ { "id": "2141582", "postDate": "02/12/2023 22:01:22", "content": "Hi everyone!
\nInspired by the great work visualising the data I've put together an app to display the direction vector, the DOM positions and animate the interactions from the first training batch. You can try it out here.
\nNote it can take awhile to load (30 seconds) but is generally quite quick once loaded so you're rewarded for the patience :)
\nA quick example
\n
Auxilary flag example
\nThere is also an auxiliary flag filter, so we can also recreate event 1583 highlighted by @jirkaborovec in this discussion trying to recreate the auxiliary true/false image in the data description:
Auxiliary = False:
\n
Auxiliary = True:
\n
One last example with lots of interactions
\nFinally quite an interesting one is 138523
Hopefully this helps someone explore the data in a bit more detail :)
\nHow was the app put together?
\nAll the code is on my github
\nDisclaimer: This is the first time I've deployed anything on google cloud run, so let me know if the app doesn't seem to working!!
", "rawMarkdown": "Hi everyone!\n\nInspired by the great work visualising the data I've put together an app to display the direction vector, the DOM positions and animate the interactions from the first training batch. You can try it out [here](https://neutrino-dash-fxxnftdb5q-uc.a.run.app/).\n\nNote it can take awhile to load (30 seconds) but is generally quite quick once loaded so you're rewarded for the patience :) \n\n**A quick example**\n\n1. As one example it is pre filtered to event 1128590 so we can recreate the great work of @pestipeti's [notebook](https://www.kaggle.com/code/pestipeti/animated-events-with-matplotlib). \n\n\n\n**Auxilary flag example**\nThere is also an auxiliary flag filter, so we can also recreate event 1583 highlighted by @jirkaborovec in [this discussion](https://www.kaggle.com/competitions/icecube-neutrinos-in-deep-ice/discussion/379915) trying to recreate the auxiliary true/false image in the data description: \n\nAuxiliary = False:\n\n\nAuxiliary = True:\n\n\n**One last example with lots of interactions**\nFinally quite an interesting one is 138523 \n\nHopefully this helps someone explore the data in a bit more detail :) \n\n\n**How was the app put together?**\n1. The charts are built using plotly\n2. The app is built using [dash](https://dash.plotly.com/introduction)\n3. I then created a docker image and use google cloud run to deploy. \n\nAll the code is on my [github](https://github.com/rhyscook92/IceCube-Neutrinos/tree/main/visualisations)\n\nDisclaimer: This is the first time I've deployed anything on google cloud run, so let me know if the app doesn't seem to working!!", "votes": null }, { "id": "2141598", "postDate": "02/12/2023 22:43:22", "content": "This is super great work. It worked fine for me - it certainly helped me visual the issue I was working on today.
\nVery nice !!!
\nThe link to your github is broken.
", "rawMarkdown": "This is super great work. It worked fine for me - it certainly helped me visual the issue I was working on today.\n\nVery nice !!!\n\nThe link to your github is broken.", "votes": null }, { "id": "2141602", "postDate": "02/12/2023 23:00:28", "content": "Glad to hear it helped!
\nThanks for pointing out the problem with the repo link - had it set to private but have now changed that to public!
", "rawMarkdown": "Glad to hear it helped!\n\nThanks for pointing out the problem with the repo link - had it set to private but have now changed that to public!", "votes": null }, { "id": "2141923", "postDate": "02/13/2023 07:28:52", "content": "Mate, this is awesome. Thank you!
", "rawMarkdown": "Mate, this is awesome. Thank you!", "votes": null }, { "id": "2142145", "postDate": "02/13/2023 10:47:10", "content": "This is awesome! 😍 Thanks for sharing
", "rawMarkdown": "This is awesome! 😍 Thanks for sharing", "votes": null }, { "id": "2143425", "postDate": "02/14/2023 08:47:17", "content": "Very great work!
", "rawMarkdown": "Very great work!", "votes": null }, { "id": "2147754", "postDate": "02/16/2023 21:12:22", "content": "Very nice to see the time element. Adds a lot of insight.
", "rawMarkdown": "Very nice to see the time element. Adds a lot of insight.", "votes": null }, { "id": "2150498", "postDate": "02/19/2023 10:07:27", "content": "Terrific work, thanks for sharing @rhysie
", "rawMarkdown": "Terrific work, thanks for sharing @rhysie", "votes": null } ], "comments": [ { "id": 2141598, "author_name": "pcjimmmy", "author_url": "", "post_date": "02/12/2023 22:43:22", "content": "This is super great work. It worked fine for me - it certainly helped me visual the issue I was working on today.
\nVery nice !!!
\nThe link to your github is broken.
", "votes": null, "replies": [ { "id": 2141602, "author_name": "rhysie", "author_url": "", "post_date": "02/12/2023 23:00:28", "content": "Glad to hear it helped!
\nThanks for pointing out the problem with the repo link - had it set to private but have now changed that to public!
", "votes": null, "replies": [] } ] }, { "id": 2141923, "author_name": "lockeza", "author_url": "", "post_date": "02/13/2023 07:28:52", "content": "Mate, this is awesome. Thank you!
", "votes": null, "replies": [] }, { "id": 2142145, "author_name": "pellerphys", "author_url": "", "post_date": "02/13/2023 10:47:10", "content": "This is awesome! 😍 Thanks for sharing
", "votes": null, "replies": [] }, { "id": 2143425, "author_name": "aristhene", "author_url": "", "post_date": "02/14/2023 08:47:17", "content": "Very great work!
", "votes": null, "replies": [] }, { "id": 2147754, "author_name": "edguy99", "author_url": "", "post_date": "02/16/2023 21:12:22", "content": "Very nice to see the time element. Adds a lot of insight.
", "votes": null, "replies": [] }, { "id": 2150498, "author_name": "sstarz", "author_url": "", "post_date": "02/19/2023 10:07:27", "content": "Terrific work, thanks for sharing @rhysie
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2141582": "Hi everyone!\n\nInspired by the great work visualising the data I've put together an app to display the direction vector, the DOM positions and animate the interactions from the first training batch. You can try it out [here](https://neutrino-dash-fxxnftdb5q-uc.a.run.app/).\n\nNote it can take awhile to load (30 seconds) but is generally quite quick once loaded so you're rewarded for the patience :) \n\n**A quick example**\n\n1. As one example it is pre filtered to event 1128590 so we can recreate the great work of @pestipeti's [notebook](https://www.kaggle.com/code/pestipeti/animated-events-with-matplotlib). \n\n\n\n**Auxilary flag example**\nThere is also an auxiliary flag filter, so we can also recreate event 1583 highlighted by @jirkaborovec in [this discussion](https://www.kaggle.com/competitions/icecube-neutrinos-in-deep-ice/discussion/379915) trying to recreate the auxiliary true/false image in the data description: \n\nAuxiliary = False:\n\n\nAuxiliary = True:\n\n\n**One last example with lots of interactions**\nFinally quite an interesting one is 138523 \n\nHopefully this helps someone explore the data in a bit more detail :) \n\n\n**How was the app put together?**\n1. The charts are built using plotly\n2. The app is built using [dash](https://dash.plotly.com/introduction)\n3. I then created a docker image and use google cloud run to deploy. \n\nAll the code is on my [github](https://github.com/rhyscook92/IceCube-Neutrinos/tree/main/visualisations)\n\nDisclaimer: This is the first time I've deployed anything on google cloud run, so let me know if the app doesn't seem to working!!", "2141598": "This is super great work. It worked fine for me - it certainly helped me visual the issue I was working on today.\n\nVery nice !!!\n\nThe link to your github is broken.", "2141602": "Glad to hear it helped!\n\nThanks for pointing out the problem with the repo link - had it set to private but have now changed that to public!", "2141923": "Mate, this is awesome. Thank you!", "2142145": "This is awesome! 😍 Thanks for sharing", "2143425": "Very great work!", "2147754": "Very nice to see the time element. Adds a lot of insight.", "2150498": "Terrific work, thanks for sharing @rhysie" }, "source": "meta" }