Update loader a bit

gfs-reforecast.py

@@ -23,8 +23,7 @@ import datasets
 _CITATION = """\
 @InProceedings{ocf:gfs,
 title = {GFS Forecast Dataset},
-author={Jacob Bieker
-},
+author={Jacob Bieker},
 year={2022}
 }
 """
@@ -42,18 +41,12 @@ _LICENSE = "US Government data, Open license, no restrictions"
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
-    "2021": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2021/2021.zarr.zip",
-    "2017": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2017/2017.zarr.zip",
-    "2016": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016/2016.zarr.zip",
-    "2018": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2018/2018.zarr.zip",
-    "2019": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2019/2019.zarr.zip",
-    "2022": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2022/2022.zarr.zip",
+    "GFSv16": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/forecasts/GFSv16/*.zarr.zip",
+    "raw": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/raw/*.zarr.zip",
+    "analysis": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/analysis/*.zarr.zip",
 }
-# Add default training one, train on all before 2020, validate on 2021, test on 2022
-_URLS["default"] = {"train": [_URLS["2016"], _URLS["2017"], _URLS["2018"], _URLS["2019"]], "valid": [_URLS["2021"]], "test": [_URLS["2022"]]}
-_URLS["default_sequence"] = _URLS["default"]
 
-class MRMS(datasets.GeneratorBasedBuilder):
+class GFEReforecastDataset(datasets.GeneratorBasedBuilder):
     """Archival MRMS Precipitation Rate Radar data for the continental US, covering most of 2016-2022."""
 
     VERSION = datasets.Version("1.0.0")
@@ -71,16 +64,21 @@ class MRMS(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name="analysis", version=VERSION, description="FNL 0.25 degree Analysis files"),
-        datasets.BuilderConfig(name="default_sequence", version=VERSION, description="Train on 2016-2020, validate on 2021, test on 2022, with 24 timesteps per example"),
+        datasets.BuilderConfig(name="raw_analysis", version=VERSION, description="FNL 0.25 degree Analysis files coupled with raw observations"),
+        datasets.BuilderConfig(name="gfs_v16", version=VERSION, description="GFS v16 Forecasts from April 2021 through 2022, returned as a 696 channel image"),
+        datasets.BuilderConfig(name="raw_gfs_v16", version=VERSION, description="GFS v16 Forecasts from April 2021 through 2022, returned as a 696 channel image, coupled with raw observations"),
+        datasets.BuilderConfig(name="gfs_v16_variables", version=VERSION, description="GFS v16 Forecasts from April 2021 through 2022 with one returned array per variable"),
     ]
 
     DEFAULT_CONFIG_NAME = "analysis"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
-        if "sequence" in self.config.name:
+        if "v16" in self.config.name:
+            # TODO Add the variables one with all 696 variables, potentially combined by level
             features = datasets.Features(
                 {
-                    "precipitation_rate": datasets.Array4D((3500,7000,2), dtype="float16"),
+                    "current_state": datasets.Array3D((721,1440,696), dtype="float32"),
+                    "next_state": datasets.Array3D((721,1440,696), dtype="float32"),
                     "timestamp": datasets.Sequence(datasets.Value("timestamp[ns]")),
                     "latitude": datasets.Sequence(datasets.Value("float32")),
                     "longitude": datasets.Sequence(datasets.Value("float32"))
@@ -90,7 +88,7 @@ class MRMS(datasets.GeneratorBasedBuilder):
         else:
             features = datasets.Features(
                 {
-                    "precipitation_rate": datasets.Array3D((3500,7000,1), dtype="float16"),
+                    "precipitation_rate": datasets.Array3D((721,1440,696), dtype="float32"),
                     "timestamp": datasets.Value("timestamp[ns]"),
                     "latitude": datasets.Sequence(datasets.Value("float32")),
                     "longitude": datasets.Sequence(datasets.Value("float32"))