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Author: pre-commit-ci[bot]

pvnet/data/site_datamodule.py

@@ -2,7 +2,10 @@
 from glob import glob
 
 import xarray as xr
-from ocf_data_sampler.torch_datasets.datasets.site import SitesDataset, convert_netcdf_to_numpy_sample
+from ocf_data_sampler.torch_datasets.datasets.site import (
+    SitesDataset,
+    convert_netcdf_to_numpy_sample,
+)
 from torch.utils.data import Dataset
 
 from pvnet.data.base_datamodule import BaseDataModule

pvnet/data/uk_regional_datamodule.py

@@ -2,10 +2,11 @@
 from glob import glob
 
 import torch
-from pvnet.data.base_datamodule import BaseDataModule
 from ocf_data_sampler.torch_datasets.datasets.pvnet_uk_regional import PVNetUKRegionalDataset
 from torch.utils.data import Dataset
 
+from pvnet.data.base_datamodule import BaseDataModule
+
 
 class NumpybatchPremadeSamplesDataset(Dataset):
     """Dataset to load NumpyBatch samples"""

pvnet/models/base_model.py

@@ -18,7 +18,7 @@
 from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
 from huggingface_hub.file_download import hf_hub_download
 from huggingface_hub.hf_api import HfApi
-from ocf_datapipes.batch import BatchKey, copy_batch_to_device
+from ocf_datapipes.batch import copy_batch_to_device
 
 from pvnet.models.utils import (
     BatchAccumulator,

pvnet/models/baseline/last_value.py

@@ -1,6 +1,5 @@
 """Persistence model"""
 
-from ocf_datapipes.batch import BatchKey
 
 import pvnet
 from pvnet.models.base_model import BaseModel

pvnet/models/baseline/single_value.py

@@ -1,6 +1,5 @@
 """Average value model"""
 import torch
-from ocf_datapipes.batch import BatchKey
 from torch import nn
 
 import pvnet

pvnet/models/multimodal/multimodal.py

@@ -4,7 +4,6 @@
 from typing import Optional
 
 import torch
-from ocf_datapipes.batch import BatchKey, NWPBatchKey
 from omegaconf import DictConfig
 from torch import nn
 

pvnet/models/multimodal/multimodal_base.py

@@ -1,5 +1,4 @@
 """Base model class for multimodal model and unimodal teacher"""
-from ocf_datapipes.batch import BatchKey, NWPBatchKey
 from torchvision.transforms.functional import center_crop
 
 from pvnet.models.base_model import BaseModel

pvnet/models/multimodal/site_encoders/encoders.py

@@ -209,8 +209,8 @@ def _encode_inputs(self, x):
         # Shape: [batch size, sequence length, number of sites] -> [8, 197, 1]
         # Shape: [batch size,  station_id, sequence length,  channels] -> [8, 197, 26, 23]
         input_data = x[f"{self.input_key_to_use}"]
-        if len(input_data.shape) == 2: # one site per sample
-            input_data = input_data.unsqueeze(-1) # add dimension of 1 to end to make 3D
+        if len(input_data.shape) == 2:  # one site per sample
+            input_data = input_data.unsqueeze(-1)  # add dimension of 1 to end to make 3D
         if len(input_data.shape) == 4:  # Has multiple channels
             input_data = input_data[:, :, : self.sequence_length]
             input_data = einops.rearrange(input_data, "b id s c -> b (s c) id")

pvnet/models/multimodal/unimodal_teacher.py

@@ -7,7 +7,6 @@
 import hydra
 import torch
 import torch.nn.functional as F
-from ocf_datapipes.batch import BatchKey, NWPBatchKey
 from pyaml_env import parse_config
 from torch import nn
 

pvnet/utils.py

@@ -13,7 +13,6 @@
 import xarray as xr
 from lightning.pytorch.loggers import Logger
 from lightning.pytorch.utilities import rank_zero_only
-from ocf_datapipes.batch import BatchKey
 from ocf_datapipes.utils import Location
 from omegaconf import DictConfig, OmegaConf
 

tests/conftest.py

@@ -105,7 +105,9 @@ def sample_train_val_datamodule():
 
         file_n = 0
 
-        for file_n, file in enumerate(glob.glob("tests/test_data/presaved_samples_uk_regional/train/*.pt")):
+        for file_n, file in enumerate(
+            glob.glob("tests/test_data/presaved_samples_uk_regional/train/*.pt")
+        ):
             sample = torch.load(file)
 
             for i in range(n_duplicates):
@@ -204,14 +206,12 @@ def site_encoder_model_kwargs():
     )
     return kwargs
 
+
 @pytest.fixture()
 def site_encoder_model_kwargs_dsampler():
     # Used to test site encoder model on PV data
     kwargs = dict(
-        sequence_length=60 // 15 - 1,
-        num_sites=1,
-        out_features=128,
-        target_key_to_use="site"
+        sequence_length=60 // 15 - 1, num_sites=1, out_features=128, target_key_to_use="site"
     )
     return kwargs
 

tests/models/multimodal/site_encoders/test_encoders.py

@@ -58,4 +58,6 @@ def test_simplelearnedaggregator_backward(sample_pv_batch, site_encoder_model_kw
 
 
 def test_singleattentionnetwork_backward(sample_site_batch, site_encoder_model_kwargs_dsampler):
-    _test_model_backward(sample_site_batch, SingleAttentionNetwork, site_encoder_model_kwargs_dsampler)
+    _test_model_backward(
+        sample_site_batch, SingleAttentionNetwork, site_encoder_model_kwargs_dsampler
+    )

tests/test_data/presaved_samples_site/data_configuration.yaml

@@ -3,7 +3,6 @@ general:
   name: windnet_data_sampler
 
 input_data:
-
   site:
     time_resolution_minutes: 15
     interval_start_minutes: -60
@@ -13,7 +12,6 @@ input_data:
     dropout_timedeltas_minutes: null
     dropout_fraction: 0 # Fraction of samples with dropout
 
-
   # nwp:
   #   mo_global:
   #     provider: mo_global
@@ -35,7 +33,7 @@ input_data:
   #     # How long after the NWP init-time are we still willing to use this forecast
   #     # If null we use each init-time for all steps it covers
   #     max_staleness_minutes: null
-  
+
   nwp:
     ecmwf:
       provider: ecmwf

tests/test_data/presaved_samples_site/datamodule.yaml

@@ -7,8 +7,8 @@ sample_output_dir: /mnt/storage_ssd_4tb/site_batches_d_sampler_output_multiple_s
 num_val_samples: 64
 num_train_samples: 128
 train_period:
-- '2022-03-01'
-- '2022-06-01'
+  - "2022-03-01"
+  - "2022-06-01"
 val_period:
-- '2022-06-02'
-- '2022-07-01'
+  - "2022-06-02"
+  - "2022-07-01"