Introduced datetime forcing calculation as seperate script

create_forcings.py

@@ -0,0 +1,78 @@
+# Standard library
+import argparse
+
+# Third-party
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+# First-party
+from neural_lam import config
+
+
+def get_seconds_in_year(year):
+    start_of_year = pd.Timestamp(f"{year}-01-01")
+    start_of_next_year = pd.Timestamp(f"{year + 1}-01-01")
+    return (start_of_next_year - start_of_year).total_seconds()
+
+
+def calculate_datetime_forcing(timesteps):
+    hours_of_day = xr.DataArray(timesteps.dt.hour, dims=["time"])
+    seconds_into_year = xr.DataArray(
+        [
+            (
+                pd.Timestamp(dt_obj)
+                - pd.Timestamp(f"{pd.Timestamp(dt_obj).year}-01-01")
+            ).total_seconds()
+            for dt_obj in timesteps.values
+        ],
+        dims=["time"],
+    )
+    year_seconds = xr.DataArray(
+        [
+            get_seconds_in_year(pd.Timestamp(dt_obj).year)
+            for dt_obj in timesteps.values
+        ],
+        dims=["time"],
+    )
+    hour_angle = (hours_of_day / 12) * np.pi
+    year_angle = (seconds_into_year / year_seconds) * 2 * np.pi
+    datetime_forcing = xr.Dataset(
+        {
+            "hour_sin": np.sin(hour_angle),
+            "hour_cos": np.cos(hour_angle),
+            "year_sin": np.sin(year_angle),
+            "year_cos": np.cos(year_angle),
+        },
+        coords={"time": timesteps},
+    )
+    datetime_forcing = (datetime_forcing + 1) / 2
+    return datetime_forcing
+
+
+def main():
+    """Main function for creating the datetime forcing and boundary mask."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--data_config", type=str, default="neural_lam/data_config.yaml"
+    )
+    parser.add_argument("--zarr_path", type=str, default="forcings.zarr")
+    args = parser.parse_args()
+
+    config_loader = config.Config.from_file(args.data_config)
+    dataset = config_loader.open_zarr("state")
+    datetime_forcing = calculate_datetime_forcing(timesteps=dataset.time)
+
+    # Expand dimensions to match the target dataset
+    datetime_forcing_expanded = datetime_forcing.expand_dims(
+        {"y": dataset.y, "x": dataset.x}
+    )
+
+    datetime_forcing_expanded.to_zarr(args.zarr_path, mode="w")
+    print(f"Datetime forcing saved to {args.zarr_path}")
+
+    dataset
+
+
+if __name__ == "__main__":
+    main()

neural_lam/data_config.yaml

@@ -51,11 +51,22 @@ forcing:
       lat_lon_names:
         lon: lon
         lat: lat
+    - path: "forcings.zarr"
+      dims:
+        time: time
+        level: null
+        x: x
+        y: y
+        grid: null
   surface_vars:
     - cape_column # just as a technical test
     - icei0m
     - vis0m
     - xhail0m
+    - hour_cos
+    - hour_sin
+    - year_cos
+    - year_sin
   surface_units:
     - J/kg
     - kg/m^2 # just as a technical test :)