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Quentin Febvre 529a5fa32e | 1 month ago | |
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config | 9 months ago | |
contrib | 1 month ago | |
data | 1 year ago | |
src | 9 months ago | |
.gitignore | 1 month ago | |
README.md | 1 year ago | |
environment.yaml | 1 month ago | |
main.py | 11 months ago | |
my_xps.csv | 1 year ago |
git clone https://github.com/CIA-Oceanix/4dvarnet-starter.git
cd 4dvarnet-starter
conda install -c conda-forge mamba
conda create -n 4dvarnet-starter
conda activate 4dvarnet-starter
mamba env update -f environment.yaml
From the directory
wget https://s3.eu-central-1.wasabisys.com/sla-data-registry/6d/206c6be2dfe0edf1a53c29029ed239 -O data/natl_gf_w_5nadirs.nc
The model uses hydra see [#useful-links]
python main.py xp=base
A bigger model has been trained using the command
python main.py xp=base +params=bigger_model
You can find pre-trained weights here
The test metrics of this model are ([see here for the details])(https://github.com/ocean-data-challenges/2020a_SSH_mapping_NATL60):
osse_metrics | |
---|---|
RMSE (m) | 0.0211406 |
λx | 0.716 |
λt | 4.681 |
μ | 0.96362 |
σ | 0.00544 |
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Python CSV
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