2023.10.3 (#173)

* update prediction

* update tests for code cov

* handle one band

openeo_processes_dask/process_implementations/ml/curve_fitting.py

@@ -25,6 +25,10 @@ def fit_curve(
         raise DimensionNotAvailable(
             f"Provided dimension ({dimension}) not found in data.dims: {data.dims}"
         )
+    bands_required = False
+    if "bands" in data.dims:
+        if len(data["bands"].values) == 1:
+            bands_required = data["bands"].values[0]
 
     try:
         # Try parsing as datetime first
@@ -81,11 +85,15 @@ def _wrap(*args, **kwargs):
         .drop_dims(["cov_i", "cov_j"])
         .to_array()
         .squeeze()
-        .transpose(*expected_dims_after)
     )
 
     fit_result.attrs = data.attrs
     fit_result = fit_result.rio.write_crs(rechunked_data.rio.crs)
+    if bands_required and not "bands" in fit_result.dims:
+        fit_result = fit_result.assign_coords(**{"bands": bands_required})
+        fit_result = fit_result.expand_dims(dim="bands")
+
+    fit_result = fit_result.transpose(*expected_dims_after)
 
     return fit_result
 
@@ -99,6 +107,7 @@ def predict_curve(
 ):
     labels_were_datetime = False
     dims_before = list(parameters.dims)
+    initial_labels = labels
 
     try:
         # Try parsing as datetime first
@@ -108,7 +117,6 @@ def predict_curve(
 
     if np.issubdtype(labels.dtype, np.datetime64):
         labels_were_datetime = True
-        initial_labels = labels
         timestep = [
             (
                 (np.datetime64(x) - np.datetime64("1970-01-01", "s"))

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "openeo-processes-dask"
-version = "2023.10.2"
+version = "2023.10.3"
 description = "Python implementations of many OpenEO processes, dask-friendly by default."
 authors = ["Lukas Weidenholzer <[email protected]>", "Sean Hoyal <[email protected]>", "Valentina Hutter <[email protected]>"]
 maintainers = ["EODC Staff <[email protected]>"]

tests/test_ml.py

@@ -83,8 +83,14 @@ def fitFunction(x, parameters):
     assert len(result.coords["y"]) == len(origin_cube.coords["y"])
     assert len(result.coords["param"]) == len(parameters)
 
+    origin_cube_B02 = origin_cube.sel(bands=["B02"])
+    result_B02 = fit_curve(
+        origin_cube_B02, parameters=parameters, function=_process, dimension="t"
+    )
+    assert "bands" in result_B02.dims
+    assert result_B02["bands"].values == "B02"
+
     labels = dimension_labels(origin_cube, origin_cube.openeo.temporal_dims[0])
-    labels = [float(l) for l in labels]
     predictions = predict_curve(
         result,
         _process,
@@ -96,7 +102,7 @@ def fitFunction(x, parameters):
     assert "param" not in predictions.dims
     assert result.rio.crs == predictions.rio.crs
 
-    labels = ["2020-02-02", "2020-03-02", "2020-04-02", "2020-05-02"]
+    labels = [0, 1, 2, 3]
     predictions = predict_curve(
         result,
         _process,