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ocf_datapipes/training/pvnet.py

@@ -5,8 +5,8 @@
 
 import numpy as np
 import xarray as xr
-from torchdata.datapipes.iter import IterDataPipe
 from torchdata.datapipes import functional_datapipe
+from torchdata.datapipes.iter import IterDataPipe
 
 from ocf_datapipes.batch import MergeNumpyModalities
 from ocf_datapipes.config.model import Configuration
@@ -92,6 +92,7 @@ def gsp_drop_national(x: Union[xr.DataArray, xr.Dataset]):
     """
     return x.where(x.gsp_id != 0, drop=True)
 
+
 @functional_datapipe("pvnet_select_pv_by_ml_id")
 class PVNetSelectPVbyMLIDIterDataPipe(IterDataPipe):
     """Select specific set of PV systems by ML ID."""
@@ -111,17 +112,15 @@ def __init__(self, source_datapipe: IterDataPipe, ml_ids: np.array):
 
     def __iter__(self):
         for x in self.source_datapipe:
-
-            # Check for missing IDs
+            # Check for missing IDs
             ml_ids_not_in_data = ~np.isin(self.ml_ids, x.ml_id)
             if ml_ids_not_in_data.any():
                 missing_ml_ids = np.array(self.ml_ids)[ml_ids_not_in_data]
                 logger.warning(
                     f"The following ML IDs were mising in the PV site-level input data: "
                     f"{missing_ml_ids}. The values for these IDs will be set to NaN."
-
                 )
-            
+
             x_filtered = (
                 # Many ML-IDs are null, so filter first
                 x.where(~x.ml_id.isnull(), drop=True)