[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

pvnet/models/multimodal/multimodal.py

@@ -356,7 +356,7 @@ def forward(self, x):
                 # Fall back to legacy keys
                 azimuth_key = f"{self._target_key}_solar_azimuth"
                 elevation_key = f"{self._target_key}_solar_elevation"
-            
+
             # Process the sun data if either key set is found
             if azimuth_key in x and elevation_key in x:
                 sun = torch.cat(

pvnet/models/multimodal/multimodal_base.py

@@ -42,14 +42,14 @@ def _adapt_batch(self, batch):
 
         if self.include_sun:
             sun_len = self.forecast_len + self.history_len + 1
-            
+
             solar_position_keys = []
             # Slife off end of solar coords
             for s in ["solar_azimuth", "solar_elevation"]:
                 if s in batch.keys():
                     solar_position_keys.append(s)
                     batch[s] = batch[s][:, :sun_len]
-            
+
             # Check for legacy keys if new keys aren't found
             if not solar_position_keys:
                 # Slice off the end of the legacy solar coords

pvnet/models/multimodal/unimodal_teacher.py

@@ -314,7 +314,7 @@ def forward(self, x, return_modes=False):
                 # Fall back to legacy keys
                 azimuth_key = f"{self._target_key}_solar_azimuth"
                 elevation_key = f"{self._target_key}_solar_elevation"
-            
+
             # Process the sun data if either key set is found
             if azimuth_key in x and elevation_key in x:
                 sun = torch.cat(

tests/models/multimodal/test_multimodal.py

@@ -54,27 +54,25 @@ def test_weighted_quantile_model_forward(multimodal_quantile_model_ignore_minute
         ["gsp_solar_azimuth", "gsp_solar_elevation"],
     ],
 )
-
-
 def test_model_with_solar_position_keys(multimodal_model, sample_batch, keys):
     """Test that the model works with both new and legacy solar position keys."""
-    azimuth_key, elevation_key = keys    
+    azimuth_key, elevation_key = keys
     batch_copy = sample_batch.copy()
-    
+
     # Clear all solar keys and add just the ones we're testing
-    for key in ["solar_azimuth", "solar_elevation", 
-                "gsp_solar_azimuth", "gsp_solar_elevation"]:
+    for key in ["solar_azimuth", "solar_elevation", "gsp_solar_azimuth", "gsp_solar_elevation"]:
         if key in batch_copy:
             del batch_copy[key]
-    
+
     # Create solar position data if needed
     import torch
+
     batch_size = sample_batch["gsp"].shape[0]
     seq_len = multimodal_model.forecast_len + multimodal_model.history_len + 1
     batch_copy[azimuth_key] = torch.rand((batch_size, seq_len))
     batch_copy[elevation_key] = torch.rand((batch_size, seq_len))
-    
+
     # Test forward and backward passes
-    y = multimodal_model(batch_copy)    
+    y = multimodal_model(batch_copy)
     assert tuple(y.shape) == (2, 16), y.shape
     y.sum().backward()

tests/models/multimodal/test_unimodal_teacher.py

@@ -113,26 +113,23 @@ def test_model_conversion(unimodal_model_kwargs, sample_batch):
         ["gsp_solar_azimuth", "gsp_solar_elevation"],
     ],
 )
-
-
 def test_unimodal_model_with_solar_position_keys(unimodal_teacher_model, sample_batch, keys):
     """Test that the unimodal teacher model works with both new and legacy solar position keys."""
-    azimuth_key, elevation_key = keys    
+    azimuth_key, elevation_key = keys
     batch_copy = sample_batch.copy()
-    
+
     # Clear all solar keys and add just the ones we're testing
-    for key in ["solar_azimuth", "solar_elevation", 
-                "gsp_solar_azimuth", "gsp_solar_elevation"]:
+    for key in ["solar_azimuth", "solar_elevation", "gsp_solar_azimuth", "gsp_solar_elevation"]:
         if key in batch_copy:
             del batch_copy[key]
-    
+
     # Create solar position data
     batch_size = sample_batch["gsp"].shape[0]
     seq_len = unimodal_teacher_model.forecast_len + unimodal_teacher_model.history_len + 1
     batch_copy[azimuth_key] = torch.rand((batch_size, seq_len))
     batch_copy[elevation_key] = torch.rand((batch_size, seq_len))
-    
+
     # Test forward and backward passes
-    y = unimodal_teacher_model(batch_copy)    
+    y = unimodal_teacher_model(batch_copy)
     assert tuple(y.shape) == (2, 16), y.shape
     y.sum().backward()