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research-rainfallradar/rainfallwrangler/package.json

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Lay out some basic scaffolding 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.
2022-05-13 18:06:15 +00:00
{
"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",
tweak licence
2022-05-13 18:08:04 +00:00
"license": "GPL-3.0-or-later",
Lay out some basic scaffolding 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.
2022-05-13 18:06:15 +00:00
"dependencies": {
Fix many bugs Many bugs remain though
2022-05-19 16:54:14 +00:00
"@tensorflow/tfjs-node-gpu": "^3.17.0",
Lay out some basic scaffolding 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.
2022-05-13 18:06:15 +00:00
"applause-cli": "^1.8.1",
"gunzip-maybe": "^1.4.2",
Implement tfrecodify subcommand. It's all still untested, but that's the next step
2022-05-19 16:15:15 +00:00
"pretty-ms": "^7.0.1",
Lay out some basic scaffolding 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.
2022-05-13 18:06:15 +00:00
"terrain50": "^1.10.1",
"tfrecord-stream": "^0.2.0"
}
}
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