made create_mesh callable as python function with arguments.

Fixed error in plotting where non-callable cartopy projection from Config was called
used current mesh generation from neural-lam instead of weather-model-graphs
finished test of training call

create_mesh.py

@@ -153,7 +153,7 @@ def prepend_node_index(graph, new_index):
     return networkx.relabel_nodes(graph, to_mapping, copy=True)
 
 
-def main():
+def main(input_args=None):
     parser = ArgumentParser(description="Graph generation arguments")
     parser.add_argument(
         "--data_config",
@@ -186,7 +186,7 @@ def main():
         default=0,
         help="Generate hierarchical mesh graph (default: 0, no)",
     )
-    args = parser.parse_args()
+    args = parser.parse_args(input_args)
 
     # Load grid positions
     config_loader = config.Config.from_file(args.data_config)

neural_lam/vis.py

@@ -87,7 +87,7 @@ def plot_prediction(
         1,
         2,
         figsize=(13, 7),
-        subplot_kw={"projection": data_config.coords_projection()},
+        subplot_kw={"projection": data_config.coords_projection},
     )
 
     # Plot pred and target
@@ -136,7 +136,7 @@ def plot_spatial_error(error, obs_mask, data_config, title=None, vrange=None):
 
     fig, ax = plt.subplots(
         figsize=(5, 4.8),
-        subplot_kw={"projection": data_config.coords_projection()},
+        subplot_kw={"projection": data_config.coords_projection},
     )
 
     ax.coastlines()  # Add coastline outlines

tests/test_mllam_dataset.py

@@ -1,15 +1,19 @@
 # Standard library
-from pathlib import Path
-
-# Third-party
-import numpy as np
-import weather_model_graphs as wmg
+import os
 
 # First-party
+from create_mesh import main as create_mesh
 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
+from train_model import main as train_model
+
+# from pathlib import Path
+# import numpy as np
+# import weather_model_graphs as wmg
+
+
+os.environ["WANDB_DISABLED"] = "true"
 
 
 def test_load_reduced_meps_dataset():
@@ -83,56 +87,69 @@ def test_load_reduced_meps_dataset():
     assert set(static_data.keys()) == required_props
 
 
-def test_create_graph_reduced_meps_dataset():
-    dataset_name = "meps_example_reduced"
-    static_dir_path = Path("data", dataset_name, "static")
-    graph_dir_path = Path("graphs", "hierarchial")
-
-    # -- Static grid node features --
-    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
-    )
-
-    # split the graph by component
-    graph_components = wmg.split_graph_by_edge_attribute(
-        graph=graph, attr="component"
-    )
+# def test_create_wmg_graph_reduced_meps_dataset():
+#     dataset_name = "meps_example_reduced"
+#     static_dir_path = Path("data", dataset_name, "static")
+#     graph_dir_path = Path("graphs", "hierarchial")
+
+#     # -- Static grid node features --
+#     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
+#     )
+
+#     # split the graph by component
+#     graph_components = wmg.split_graph_by_edge_attribute(
+#         graph=graph, attr="component"
+#     )
+
+#     m2m_graph = graph_components.pop("m2m")
+#     m2m_graph_components = wmg.split_graph_by_edge_attribute(
+#         graph=m2m_graph, attr="direction"
+#     )
+#     m2m_graph_components = {
+#         f"m2m_{name}": graph for name, graph in m2m_graph_components.items()
+#     }
+#     graph_components.update(m2m_graph_components)
+
+#     # save the graph components to disk in pytorch-geometric format
+#     for component_name, graph_component in graph_components.items():
+#         kwargs = {}
+#         wmg.save.to_pyg(
+#             graph=graph_component,
+#             name=component_name,
+#             output_directory=graph_dir_path,
+#             **kwargs,
+#         )
 
-    m2m_graph = graph_components.pop("m2m")
-    m2m_graph_components = wmg.split_graph_by_edge_attribute(
-        graph=m2m_graph, attr="direction"
-    )
-    m2m_graph_components = {
-        f"m2m_{name}": graph for name, graph in m2m_graph_components.items()
-    }
-    graph_components.update(m2m_graph_components)
 
-    # save the graph components to disk in pytorch-geometric format
-    for component_name, graph_component in graph_components.items():
-        kwargs = {}
-        wmg.save.to_pyg(
-            graph=graph_component,
-            name=component_name,
-            output_directory=graph_dir_path,
-            **kwargs,
-        )
+def test_create_graph_reduced_meps_dataset():
+    args = [
+        "--graph=hierarchical",
+        "--hierarchical=1",
+        "--data_config=data/meps_example_reduced/data_config.yaml",
+        "--levels=2",
+    ]
+    create_mesh(args)
 
 
 def test_train_model_reduced_meps_dataset():
     args = [
         "--model=hi_lam",
         "--data_config=data/meps_example_reduced/data_config.yaml",
-        "--n_workers=1",
+        "--n_workers=4",
         "--epochs=1",
         "--graph=hierarchical",
         "--hidden_dim=16",
         "--hidden_layers=1",
         "--processor_layers=1",
         "--ar_steps=1",
         "--eval=val",
-        "--wandb_project=None",
+        "--n_example_pred=0",
     ]
-    main(args)
+    train_model(args)
+
+
+test_train_model_reduced_meps_dataset()