mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-07-02 19:34:55 +00:00
Starbeamrainbowlabs
8a9cd6c1c0
I *really* hope this works. This is the 3rd major revision of this model. I've learnt a ton of stuff between now and my last attempt, so here's hoping that all goes well :D The basic idea behind this attempt is *Contrastive Learning*. If we don't get anything useful with this approach, then we can assume that it's not really possible / feasible. Something we need to watch out for is the variance (or rather lack thereof) in the dataset. We have 1.5M timesteps, but not a whole lot will be happening in most of those.... We may need to analyse the variance of the water depth data and extract a subsample that's more balanced.
22 lines
573 B
JSON
22 lines
573 B
JSON
{
|
|
"name": "rainfallwrangler",
|
|
"version": "1.0.0",
|
|
"description": "Wrangles rainfall radar and water depth data into something sensible.",
|
|
"main": "src/index.mjs",
|
|
"scripts": {
|
|
"test": "echo \"No tests have been implemented yet\""
|
|
},
|
|
"repository": {
|
|
"type": "git",
|
|
"url": "https://git.starbeamrainbowlabs.com/sbrl/PhD-Rainfall-Radar.git"
|
|
},
|
|
"author": "Starbeamrainbowlabs",
|
|
"license": "GPL-3.0",
|
|
"dependencies": {
|
|
"applause-cli": "^1.8.1",
|
|
"gunzip-maybe": "^1.4.2",
|
|
"terrain50": "^1.10.1",
|
|
"tfrecord-stream": "^0.2.0"
|
|
}
|
|
}
|