linting

tests/test_mllam_dataset.py

@@ -1,55 +1,50 @@
 # Standard library
 from pathlib import Path
 
-# First-party
-from neural_lam.weather_dataset import WeatherDataset
-from neural_lam.models.graph_lam import GraphLAM
-from neural_lam.utils import load_graph, load_static_data
-from neural_lam.config import Config
-from train_model import main
-
 # Third-party
 import numpy as np
 import weather_model_graphs as wmg
 
+# First-party
+from neural_lam.config import Config
+from neural_lam.utils import load_static_data
+from neural_lam.weather_dataset import WeatherDataset
+from train_model import main
+
 
 def test_load_reduced_meps_dataset():
-    data_config_file = 'data/meps_example_reduced/data_config.yaml'
-    dataset_name = 'meps_example_reduced'
+    data_config_file = "data/meps_example_reduced/data_config.yaml"
+    dataset_name = "meps_example_reduced"
 
     dataset = WeatherDataset(dataset_name="meps_example_reduced")
     config = Config.from_file(data_config_file)
 
-    var_names = config.values['dataset']['var_names']
-    var_units = config.values['dataset']['var_units']
-    var_longnames = config.values['dataset']['var_longnames']
+    var_names = config.values["dataset"]["var_names"]
+    var_units = config.values["dataset"]["var_units"]
+    var_longnames = config.values["dataset"]["var_longnames"]
 
     assert len(var_names) == len(var_longnames)
     assert len(var_names) == len(var_units)
-    
+
     # TODO: can these two variables be loaded from elsewhere?
     n_grid_static_features = 4
     n_input_steps = 2
 
-    n_forcing_features = config.values['dataset']['num_forcing_features']
+    n_forcing_features = config.values["dataset"]["num_forcing_features"]
     n_state_features = len(var_names)
     n_prediction_timesteps = dataset.sample_length - n_input_steps
-    
-    nx, ny = config.values['grid_shape_state']
+
+    nx, ny = config.values["grid_shape_state"]
     n_grid = nx * ny
 
     # check that the dataset is not empty
     assert len(dataset) > 0
 
     # get the first item
     init_states, target_states, forcing = dataset[0]
-    
+
     # check that the shapes of the tensors are correct
-    assert init_states.shape == (
-        n_input_steps, 
-        n_grid, 
-        n_state_features
-    )
+    assert init_states.shape == (n_input_steps, n_grid, n_state_features)
     assert target_states.shape == (
         n_prediction_timesteps,
         n_grid,
@@ -62,20 +57,31 @@ def test_load_reduced_meps_dataset():
     )
 
     static_data = load_static_data(dataset_name=dataset_name)
-
-    required_props = {'border_mask', 'grid_static_features', 'step_diff_mean', 'step_diff_std', 'data_mean', 'data_std', 'param_weights'}
-
+
+    required_props = {
+        "border_mask",
+        "grid_static_features",
+        "step_diff_mean",
+        "step_diff_std",
+        "data_mean",
+        "data_std",
+        "param_weights",
+    }
+
     # check the sizes of the props
     assert static_data["border_mask"].shape == (n_grid, 1)
-    assert static_data["grid_static_features"].shape == (n_grid, n_grid_static_features)
+    assert static_data["grid_static_features"].shape == (
+        n_grid,
+        n_grid_static_features,
+    )
     assert static_data["step_diff_mean"].shape == (n_state_features,)
     assert static_data["step_diff_std"].shape == (n_state_features,)
     assert static_data["data_mean"].shape == (n_state_features,)
     assert static_data["data_std"].shape == (n_state_features,)
     assert static_data["param_weights"].shape == (n_state_features,)
 
     assert set(static_data.keys()) == required_props
-    
+
 
 def test_create_graph_reduced_meps_dataset():
     dataset_name = "meps_example_reduced"
@@ -86,12 +92,13 @@ def test_create_graph_reduced_meps_dataset():
     xy_grid = np.load(static_dir_path / "nwp_xy.npy")
 
     # create the full graph
-    graph = wmg.create.archetype.create_oscarsson_hierarchical_graph(xy_grid=xy_grid)
+    graph = wmg.create.archetype.create_oscarsson_hierarchical_graph(
+        xy_grid=xy_grid
+    )
 
     # split the graph by component
     graph_components = wmg.split_graph_by_edge_attribute(
         graph=graph, attr="component"
-        # argument attribute seens to have been changed to attr, change also in weather-model-graphs/src/weather_model_graphs/save.py::to_pyg
     )
 
     m2m_graph = graph_components.pop("m2m")
@@ -116,19 +123,16 @@ def test_create_graph_reduced_meps_dataset():
 
 def test_train_model_reduced_meps_dataset():
     args = [
-        '--model=hi_lam',
-        '--data_config=data/meps_example_reduced/data_config.yaml',
-        '--n_workers=1',
-        '--epochs=1',
-        '--graph=hierarchical',
-        '--hidden_dim=16',
-        '--hidden_layers=1',
-        '--processor_layers=1',
-        '--ar_steps=1',
-        '--eval=val',
-        '--wandb_project=None',
+        "--model=hi_lam",
+        "--data_config=data/meps_example_reduced/data_config.yaml",
+        "--n_workers=1",
+        "--epochs=1",
+        "--graph=hierarchical",
+        "--hidden_dim=16",
+        "--hidden_layers=1",
+        "--processor_layers=1",
+        "--ar_steps=1",
+        "--eval=val",
+        "--wandb_project=None",
     ]
     main(args)
-
-
-