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* Added flexibility for more variables. Added normalization statistics from graphcast as json files, refactored metrics, datasets and tests as well as lightning modules. The latter have to account for nans in the data when including sea-ice-fraction and others * Added code to zero out NaNs in ground truth values for loss calculation * Renamed stats to norm, changed variable output for label wrapper, applied changes to configs * Removed import that causes error * Replaced norm scheme in graphcast statsconfig * Fixed bug in load_module due to missing stats_cfg and changed behaviour of to_xarray routines to account for levels * Added softlinks recommended by geoarches doc to .gitignore and removed pushed softlinks * Addressed issues with paramaters interpolate_nans and warning_on_nans * Added tensordict utility to test predictions for NaNs * Added documentation to the normalization module, changed name of norm file and norm module according to reviews, adapted norm files * Changes loss_coffs to compute_loss_coeffs_args for clarity, added test for graphcast stats * Added dimension names of the dataset as arguments for the dataloaders, added comment to test of era5 loaders with graphcast norm scheme * Fixed Bug in dcpp test due to missing dimension names * Added more flexibility for data naming (either names have leading zeros or not) * Refactored code to use dimension indexers for selecting dimensions and converting between tensordict and xarray, modified tests * Fixed bug with multistep rollout due to missing next-states, added torch.int64 to allow dates after 2038 * Changed behaviour of dimension indexers, added description, adapted eval_multistep and test files * fixed format problems --------- Co-authored-by: Renu Singh <renu.singh@inria.fr>
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robert-DL
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Sep 1, 2025
| cmap="plasma", | ||
| origin="upper", | ||
| extent=[-180, 180, -90, 90], | ||
| transform=ccrs.PlateCarree(), |
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Robinson gives the curved projection, but matter of taste
robert-DL
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Sep 1, 2025
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| spatial_datas = [] | ||
| for base_dir in base_dirs: | ||
| spatial_data = torch.load( |
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To be consistent, I would argue that deciding for a storage format, e.g. netcdf, seems desirable to me.
robert-DL
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Just minor comments. If we agree on the changes, we can do them in a separate merge request
| fig = plt.figure(figsize=(4, 2)) | ||
| num_plots = len(spatial_datas) | ||
| fig, axes = plt.subplots( | ||
| 1, num_plots, figsize=(6 * num_plots, 5), subplot_kw={"projection": ccrs.PlateCarree()} |
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I think single plots are better, one can align them later in latex files
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add spatial_mse to DeterministicMetrics:
eval_multistep.py supports