Better processing

gfs-reforecast.py

@@ -158,33 +158,20 @@ class GFEReforecastDataset(datasets.GeneratorBasedBuilder):
             filepaths = json.load(f)
             filepaths = ['zip:///::https://huggingface.co/datasets/openclimatefix/gfs-reforecast/resolve/main/' + f for f in filepaths]
         if "v16" in self.config.name:
-            dataset = xr.open_mfdataset(filepaths, concat_dim="reftime", combine="nested", engine='zarr', chunks={}).sortby("reftime")
-            for idx in range(0, len(dataset["reftime"].values)):
+            idx = 0
+            for f in filepaths:
+                dataset = xr.open_dataset(f, engine='zarr', chunks={})
                 try:
-                    data = dataset.isel(reftime=idx)
-                    for t in range(len(data["time"].values)-1):
-                        data_t = data.isel(time=slice(t, t+1))
-                        state_values = np.concatenate([data_t[v].values for v in sorted(data_t.data_vars)], axis=0)
-                        value = {"current_state": state_values[0],
-                                 "next_state": state_values[1],
+                    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),
                                  "timestamp": data_t["time"].values,
                                  "latitude": data_t["latitude"].values,
                                  "longitude": data_t["longitude"].values}
+                        idx += 1
                         yield idx, value
                 except:
                     # Some of the zarrs potentially have corrupted data at the end, and might fail, so this avoids that
                     continue
-        else:
-            for f in filepaths:
-                dataset = xr.open_dataset('zip:///::'+f, engine='zarr', chunks={}).sortby("time").drop_duplicates("time")
-                for key, row in enumerate(dataset["time"].values):
-                    try:
-                        data = dataset.sel(time=row)
-                        value = {"precipitation_rate": data["unknown"].values,
-                                 "timestamp": data["time"].values,
-                                 "latitude": data["latitude"].values,
-                                 "longitude": data["longitude"].values}
-                        yield key, value
-                    except:
-                        # Some of the zarrs potentially have corrupted data at the end, and might fail, so this avoids that
-                        continue