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

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openclimatefix · Feb 6, 2024 · d6a1035 · d6a1035
1 parent 1e7b648
commit d6a1035
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Showing 4 changed files with 10 additions and 8 deletions.
diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
@@ -241,7 +241,7 @@ def __init__(
         output_quantiles: Optional[list[float]] = None,
         target_key: str = "gsp",
         interval_minutes: int = 30,
-        timestep_intervals_to_plot: Optional[list[int]] = None
+        timestep_intervals_to_plot: Optional[list[int]] = None,
     ):
         """Abtstract base class for PVNet submodels.
 
@@ -261,7 +261,9 @@ def __init__(
         self._target_key = BatchKey[f"{target_key}"]
         if timestep_intervals_to_plot is not None:
             for interval in timestep_intervals_to_plot:
-                assert type(interval) in [list, tuple] and len(interval) == 2, ValueError(f"timestep_intervals_to_plot must be a list of tuples or lists of length 2, but got {timestep_intervals_to_plot=}")
+                assert type(interval) in [list, tuple] and len(interval) == 2, ValueError(
+                    f"timestep_intervals_to_plot must be a list of tuples or lists of length 2, but got {timestep_intervals_to_plot=}"
+                )
         self.time_step_intervals_to_plot = timestep_intervals_to_plot
 
         # Model must have lr to allow tuning
@@ -453,7 +455,7 @@ def training_step(self, batch, batch_idx):
         # Make all -1 values 0.0
         batch[self._target_key] = batch[self._target_key].clamp(min=0.0)
         y_hat = self(batch)
-        y = batch[self._target_key][:, -self.forecast_len:, 0]
+        y = batch[self._target_key][:, -self.forecast_len :, 0]
 
         losses = self._calculate_common_losses(y, y_hat)
         losses = {f"{k}/train": v for k, v in losses.items()}
@@ -472,7 +474,7 @@ def validation_step(self, batch: dict, batch_idx):
         batch[self._target_key] = batch[self._target_key].clamp(min=0.0)
         y_hat = self(batch)
         # Sensor seems to be in batch, station, time order
-        y = batch[self._target_key][:, -self.forecast_len:, 0]
+        y = batch[self._target_key][:, -self.forecast_len :, 0]
 
         losses = self._calculate_common_losses(y, y_hat)
         losses.update(self._calculate_val_losses(y, y_hat))
@@ -559,7 +561,7 @@ def test_step(self, batch, batch_idx):
         # Make all -1 values 0.0
         batch[self._target_key] = batch[self._target_key].clamp(min=0.0)
         y_hat = self(batch)
-        y = batch[self._target_key][:, -self.forecast_len:, 0]
+        y = batch[self._target_key][:, -self.forecast_len :, 0]
 
         losses = self._calculate_common_losses(y, y_hat)
         losses.update(self._calculate_val_losses(y, y_hat))

diff --git a/pvnet/models/multimodal/deep_supervision.py b/pvnet/models/multimodal/deep_supervision.py
@@ -266,7 +266,7 @@ def multi_mode_forward(self, x):
     def training_step(self, batch, batch_idx):
         """Training step"""
         y_hats = self.multi_mode_forward(batch)
-        y = batch[BatchKey.gsp][:, -self.forecast_len:, 0]
+        y = batch[BatchKey.gsp][:, -self.forecast_len :, 0]
 
         losses = self._calculate_common_losses(y, y_hats["all"])
         losses = {f"{k}/train": v for k, v in losses.items()}

diff --git a/pvnet/models/multimodal/multimodal.py b/pvnet/models/multimodal/multimodal.py
@@ -133,7 +133,7 @@ def __init__(
             output_quantiles=output_quantiles,
             target_key=target_key,
             interval_minutes=interval_minutes,
-            timestep_intervals_to_plot=timestep_intervals_to_plot
+            timestep_intervals_to_plot=timestep_intervals_to_plot,
         )
 
         # Number of features expected by the output_network

diff --git a/pvnet/models/multimodal/weather_residual.py b/pvnet/models/multimodal/weather_residual.py
@@ -295,7 +295,7 @@ def multi_mode_forward(self, x):
     def training_step(self, batch, batch_idx):
         """Run training step"""
         y_hats = self.multi_mode_forward(batch)
-        y = batch[BatchKey.gsp][:, -self.forecast_len:, 0]
+        y = batch[BatchKey.gsp][:, -self.forecast_len :, 0]
 
         losses = self._calculate_common_losses(y, y_hats["weather_out"])
         losses = {f"{k}/train": v for k, v in losses.items()}