Add raw observation Features

gfs-reforecast.py

@@ -73,10 +73,10 @@ class GFEReforecastDataset(datasets.GeneratorBasedBuilder):
     DEFAULT_CONFIG_NAME = "gfs_v16"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
+        features = {}
         if "v16" in self.config.name:
             # TODO Add the variables one with all 696 variables, potentially combined by level
-            features = datasets.Features(
-                {
+            features = {
                     "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]")),
@@ -84,20 +84,27 @@ class GFEReforecastDataset(datasets.GeneratorBasedBuilder):
                     "longitude": datasets.Sequence(datasets.Value("float32"))
                     # These are the features of your dataset like images, labels ...
                 }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "precipitation_rate": datasets.Array3D((721,1440,696), dtype="float32"),
-                    "timestamp": datasets.Value("timestamp[ns]"),
+        elif "analysis" in self.config.name:
+            # TODO Add the variables one with all 322 variables, potentially combined by level
+            features = {
+                    "current_state": datasets.Array3D((721,1440,322), dtype="float32"),
+                    "next_state": datasets.Array3D((721,1440,322), dtype="float32"),
+                    "timestamp": datasets.Sequence(datasets.Value("timestamp[ns]")),
                     "latitude": datasets.Sequence(datasets.Value("float32")),
                     "longitude": datasets.Sequence(datasets.Value("float32"))
                     # These are the features of your dataset like images, labels ...
                 }
-            )
         if "raw" in self.config.name:
-            # Add the raw observation features
-            raw_features = {}
+            # Add the raw observation features, capping at 256,000 observations, padding if not enough
+            raw_features = {"observations": datasets.Array2D((256000,1), dtype="float32"),
+                            "observation_type": datasets.Array2D((256000,1), dtype="string"),
+                            "observation_lat": datasets.Array2D((256000,1), dtype="float32"),
+                            "observation_lon": datasets.Array2D((256000,1), dtype="float32"),
+                            }
+            features = features.update(raw_features)
+
+        features = datasets.Features(features)
+
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -165,8 +172,8 @@ class GFEReforecastDataset(datasets.GeneratorBasedBuilder):
                     for t in range(len(dataset["time"].values)-1):
                         data_t = dataset.isel(time=t)
                         data_t1 = dataset.isel(time=(t+1))
-                        value = {"current_state": np.concatenate([data_t[v].values for v in sorted(data_t.data_vars)], axis=0),
-                                 "next_state": np.concatenate([data_t1[v].values for v in sorted(data_t.data_vars)], axis=0),
+                        value = {"current_state": np.stack([data_t[v].values for v in sorted(data_t.data_vars)], axis=2),
+                                 "next_state": np.stack([data_t1[v].values for v in sorted(data_t.data_vars)], axis=2),
                                  "timestamp": data_t["time"].values,
                                  "latitude": data_t["latitude"].values,
                                  "longitude": data_t["longitude"].values}