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

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

pvnet/models/base_model.py

@@ -6,8 +6,8 @@
 from typing import Dict, Optional, Union
 
 import hydra
-import matplotlib.pyplot as plt
 import lightning.pytorch as pl
+import matplotlib.pyplot as plt
 import pandas as pd
 import torch
 import torch.nn.functional as F
@@ -490,11 +490,11 @@ def validation_step(self, batch: dict, batch_idx):
             {"mae_vs_timestep": wandb.plot.line(table, "timestep", "MAE", title="MAE vs Timestep")}
         )
 
-        #self.log_dict(
+        # self.log_dict(
         #    logged_losses,
         #    on_step=False,
         #    on_epoch=True,
-        #)
+        # )
 
         accum_batch_num = batch_idx // self.trainer.accumulate_grad_batches
 
@@ -531,11 +531,13 @@ def validation_step(self, batch: dict, batch_idx):
                     y_hat,
                     quantiles=self.output_quantiles,
                     key_to_plot=self._target_key_name,
-                    timesteps_to_plot=[6,12] # 1:30 to 3 hours ahead
+                    timesteps_to_plot=[6, 12],  # 1:30 to 3 hours ahead
                 )
                 self.logger.experiment.log(
                     {
-                        f"val_forecast_samples/batch_idx_{accum_batch_num}_1.5_to_3hr": wandb.Image(fig),
+                        f"val_forecast_samples/batch_idx_{accum_batch_num}_1.5_to_3hr": wandb.Image(
+                            fig
+                        ),
                     }
                 )
                 plt.close(fig)
@@ -546,11 +548,13 @@ def validation_step(self, batch: dict, batch_idx):
                     y_hat,
                     quantiles=self.output_quantiles,
                     key_to_plot=self._target_key_name,
-                    timesteps_to_plot=[60, 156]  # 15 to 39 hours ahead
+                    timesteps_to_plot=[60, 156],  # 15 to 39 hours ahead
                 )
                 self.logger.experiment.log(
                     {
-                        f"val_forecast_samples/batch_idx_{accum_batch_num}_15_to_39hr": wandb.Image(fig),
+                        f"val_forecast_samples/batch_idx_{accum_batch_num}_15_to_39hr": wandb.Image(
+                            fig
+                        ),
                     }
                 )
                 plt.close(fig)

pvnet/utils.py

@@ -3,8 +3,8 @@
 import os
 import warnings
 from collections.abc import Sequence
-
 from typing import Optional
+
 import lightning.pytorch as pl
 import matplotlib.pyplot as plt
 import numpy as np
@@ -243,15 +243,22 @@ def finish(
             wandb.finish()
 
 
-def plot_batch_forecasts(batch, y_hat, batch_idx=None, quantiles=None, key_to_plot: str = "gsp", timesteps_to_plot: Optional[list[int]] = None):
+def plot_batch_forecasts(
+    batch,
+    y_hat,
+    batch_idx=None,
+    quantiles=None,
+    key_to_plot: str = "gsp",
+    timesteps_to_plot: Optional[list[int]] = None,
+):
     """Plot a batch of data and the forecast from that batch"""
 
     def _get_numpy(key):
         return batch[key].cpu().numpy().squeeze()
 
     y_key = BatchKey[f"{key_to_plot}"]
     y_id_key = BatchKey[f"{key_to_plot}_id"]
-    t0_idx_key = BatchKey[f"{key_to_plot}_t0_idx"]
+    BatchKey[f"{key_to_plot}_t0_idx"]
     time_utc_key = BatchKey[f"{key_to_plot}_time_utc"]
     y = batch[y_key][:, :, 0].cpu().numpy()  # Select the one it is trained on
     y_hat = y_hat.cpu().numpy()