Small bugfixes (#60)

* Fix CF attrs for projected coordinates

* Fix range selection for custim dimensions

* lint

* More test cases

tests/test_cf_router.py

@@ -123,11 +123,11 @@ def test_cf_position_query(cf_client, cf_air_dataset, cf_temp_dataset):
     axes = data["domain"]["axes"]
 
     npt.assert_array_almost_equal(
-        axes["longitude"]["values"],
+        axes["x"]["values"],
         [[x]],
     ), "Did not select nearby x coordinate"
     npt.assert_array_almost_equal(
-        axes["latitude"]["values"],
+        axes["y"]["values"],
         [[y]],
     ), "Did not select a nearby y coordinate"
 

tests/test_select.py

@@ -87,6 +87,39 @@ def test_select_query(regular_xy_dataset):
         "Time is incorrect",
     )
 
+    custom_dim_ds = xr.Dataset(
+        coords={
+            "lat": np.arange(45, 47),
+            "lon": np.arange(200, 202),
+            "elevation": np.arange(100, 105),
+            "step": pd.timedelta_range("0 days", periods=72, freq="1H"),
+        },
+        data_vars={
+            "air": (("lat", "lon", "elevation", "step"), np.random.rand(2, 2, 5, 72)),
+        },
+    )
+
+    query = EDRQuery(
+        coords="POINT(201 46)",
+        parameters="air",
+        method="linear",
+    )
+    ds = query.select(custom_dim_ds, {"step": "0 hours/10 hours", "elevation": "101"})
+    assert ds["air"].shape == (2, 2, 1, 11), "Dataset shape is incorrect"
+    npt.assert_array_equal(
+        ds["step"],
+        pd.timedelta_range("0 days", periods=11, freq="1H"),
+    )
+    npt.assert_equal(ds["elevation"].values, 101)
+
+    ds = query.select(custom_dim_ds, {"step": "1 hours", "elevation": "101/103"})
+    assert ds["air"].shape == (2, 2, 3, 1), "Dataset shape is incorrect"
+    npt.assert_array_equal(
+        ds["step"],
+        pd.timedelta_range("1 hours", periods=1, freq="1H"),
+    )
+    npt.assert_equal(ds["elevation"].values, np.array([101, 102, 103]))
+
 
 def test_select_query_error(regular_xy_dataset):
     query = EDRQuery(
@@ -162,11 +195,11 @@ def test_select_position_projected_xy(projected_xy_dataset):
 
     projected_ds = project_dataset(ds, query.crs)
     (
-        npt.assert_approx_equal(projected_ds.longitude.values, 64.59063409),
+        npt.assert_approx_equal(projected_ds.cf["X"].values, 64.59063409),
         "Longitude is incorrect",
     )
     (
-        npt.assert_approx_equal(projected_ds.latitude.values, 66.66454929),
+        npt.assert_approx_equal(projected_ds.cf["Y"].values, 66.66454929),
         "Latitude is incorrect",
     )
     (

xpublish_edr/geometry/common.py

@@ -117,10 +117,12 @@ def project_dataset(ds: xr.Dataset, query_crs: str) -> xr.Dataset:
     ds = ds.drop_vars(coords_to_drop)
 
     # Create the new dataset with vectorized coordinates
+    x_var = xr.Variable(dims=target_dims, data=x, attrs=target_x_coord)
+    y_var = xr.Variable(dims=target_dims, data=y, attrs=target_y_coord)
     ds = ds.assign_coords(
         {
-            target_x_coord_name: (target_dims, x),
-            target_y_coord_name: (target_dims, y),
+            target_x_coord_name: x_var,
+            target_y_coord_name: y_var,
         },
     )
 

xpublish_edr/query.py

@@ -81,25 +81,32 @@ def select(self, ds: xr.Dataset, query_params: dict) -> xr.Dataset:
             if query_param in edr_query_params:
                 del query_params[query_param]
 
+        sel_params = {}
+        sliced_sel_params = {}
         for key, value in query_params.items():
-            split_value = value.split("/")
+            split_value = [float(v) if v.isnumeric() else v for v in value.split("/")]
             if len(split_value) == 1:
-                query_params[key] = [split_value[0]]
+                sel_params[key] = [split_value[0]]
             elif len(split_value) == 2:
-                query_params[key] = slice(split_value[0], split_value[1])
+                sliced_sel_params[key] = slice(split_value[0], split_value[1])
             else:
                 raise ValueError(f"Too many values for selecting {key}")
 
+        # We separate the slice selection from the single value selection in order to take
+        # advantage of selection method which breaks when mixing the two
+        if len(sliced_sel_params) > 0:
+            ds = ds.sel(sliced_sel_params)
+
         if self.method == "nearest":
-            ds = ds.sel(query_params, method=self.method)
+            ds = ds.sel(sel_params, method=self.method)
         else:
             # Interpolation may not be supported for all possible selection
             # parameters, so we provide a fallback to xarray's nearest selection
             try:
-                ds = ds.interp(query_params, method=self.method)
+                ds = ds.interp(sel_params, method=self.method)
             except Exception as e:
                 logger.warning(f"Interpolation failed: {e}, falling back to selection")
-                ds = ds.sel(query_params, method="nearest")
+                ds = ds.sel(sel_params, method="nearest")
 
         return ds