update sample helper used for bilinear interpolation

src/ml_downscaling_emulator/bin/__init__.py

@@ -1,9 +1,9 @@
 import typer
 
-from . import evaluate, postprocess
+from . import postprocess, sample
 
 app = typer.Typer()
-app.add_typer(evaluate.app, name="evaluate")
+app.add_typer(sample.app, name="sample")
 app.add_typer(postprocess.app, name="postprocess")
 
 

src/ml_downscaling_emulator/bin/evaluate.py → src/ml_downscaling_emulator/bin/sample.py

@@ -72,14 +72,18 @@ def _sample_id(variable: str, eval_ds: xr.Dataset) -> xr.Dataset:
 @app.command()
 @Timer(name="sample", text="{name}: {minutes:.1f} minutes", logger=logging.info)
 @slack_sender(webhook_url=os.getenv("KK_SLACK_WH_URL"), channel="general")
-def sample_id(
+def as_input(
     workdir: Path,
     dataset: str = typer.Option(...),
     variable: str = "pr",
     split: str = "val",
     ensemble_member: str = DEFAULT_ENSEMBLE_MEMBER,
 ):
+    """
+    Use a given variable from the dataset to create a file of prediction samples.
 
+    Commonly used to create samples based on an already processed variable like using a bilinearly interpolated coarse resolution variable as the predicted "high-resolution" value directly.
+    """
     output_dirpath = samples_path(
         workdir=workdir,
         checkpoint=f"epoch-0",

tests/ml_downscaling_emulator/deterministic/test_sampling.py → tests/ml_downscaling_emulator/bin/test_sample.py

@@ -1,14 +1,14 @@
 import xarray as xr
 
-from ml_downscaling_emulator.deterministic.sampling import sample_id
+from ml_downscaling_emulator.bin.sample import _sample_id
 
 
 def test_sample_id(dataset: xr.Dataset):
-    """Ensure the sample_id function creates a set of predictions using the values of the given variable."""
+    """Ensure the _sample_id bin function creates a set of predictions using the values of the given variable."""
 
     variable = "linpr"
     em_dataset = dataset.sel(ensemble_member=["01"])
-    xr_samples = sample_id(variable, em_dataset)
+    xr_samples = _sample_id(variable, em_dataset)
 
     assert (xr_samples["pred_pr"].values == em_dataset["linpr"].values).all()
     for dim in ["time", "grid_latitude", "grid_longitude"]: