Add tests for Xarray io functions

src/coincident/datasets/planetary_computer.py

@@ -19,15 +19,15 @@
 
 @dataclass
 class COP30(Dataset):
-    """Essential metadata for Copernicus DEM"""
+    """Essential metadata and data access for Copernicus DEM"""
 
     alias: str = "cop30"
     has_stac_api: bool = True
     collections: list[str] = field(default_factory=lambda: ["cop-dem-glo-30"])
     search: str = STACAPI
-    start: str | None = None  # NOTE: has 'representative' datetime of 2021-04-22
+    start: str | None = None  # Copernicus DEM has 'representative' datetime: 2021-04-22
     end: str | None = None
-    type: str = "sar"
+    type: str = "dem"
     provider: str = "microsoft"
 
 

tests/test_xarray.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import pytest
+import xarray as xr
+
+import coincident
+from coincident.io.xarray import plot_esa_worldcover, to_dataset
+
+# These tests are based on the workflow from https://coincident.readthedocs.io/en/latest/examples/contextual_data.html
+# I couldn't think of a way to generalized these tests since the
+# xarray funcs are pretty tailored to COP30 and ESA WC
+
+# mypy doesn't like the resolution argument in to_dataset
+# Argument "resolution" to "to_dataset" has incompatible type "float"; expected "dict[str, Any]"  [arg-type]
+
+@pytest.fixture
+def search_aoi():
+    """Fixture to load a simplified AOI from realistic lidar data."""
+    workunit = "CO_WestCentral_2019"
+    df_wesm = coincident.search.wesm.read_wesm_csv()
+    gf_lidar = coincident.search.wesm.load_by_fid(
+        df_wesm[df_wesm.workunit == workunit].index
+    )
+    return gf_lidar.simplify(0.01)
+
+
+@pytest.mark.network
+def test_to_dataset_with_cop30(search_aoi):
+    """Test `to_dataset` functionality with COP30 dataset."""
+    gf_cop30 = coincident.search.search(
+        dataset="cop30",
+        intersects=search_aoi,
+    )
+    ds = to_dataset(
+        gf_cop30,
+        aoi=search_aoi,
+        resolution=0.00081,  # ~90m
+        mask=True,
+    ).compute()
+    assert isinstance(ds, xr.Dataset), "Expected output to be an xarray Dataset."
+    assert "data" in ds.data_vars, "Expected 'data' variable in the Dataset."
+
+
+@pytest.mark.network
+def test_to_dataset_with_worldcover(search_aoi):
+    """Test `to_dataset` functionality with WorldCover dataset."""
+    gf_wc = coincident.search.search(
+        dataset="worldcover",
+        intersects=search_aoi,
+        datetime=["2020"],
+    )
+    ds = to_dataset(
+        gf_wc,
+        bands=["map"],
+        aoi=search_aoi,
+        resolution=0.00081,  # ~90m
+        mask=True,
+    ).compute()
+    ds = ds.rename(map="landcover")
+    assert isinstance(ds, xr.Dataset), "Expected output to be an xarray Dataset."
+    assert "landcover" in ds.data_vars, "Expected 'landcover' variable in the Dataset."
+
+
+# Tests for `plot_esa_worldcover`
+@pytest.mark.network
+def test_plot_esa_worldcover_valid(search_aoi):
+    """Test `plot_esa_worldcover` with valid WorldCover dataset."""
+    gf_wc = coincident.search.search(
+        dataset="worldcover",
+        intersects=search_aoi,
+        datetime=["2020"],
+    )
+    ds = to_dataset(
+        gf_wc,
+        bands=["map"],
+        aoi=search_aoi,
+        resolution=0.00081,  # ~90m
+        mask=True,
+    ).compute()
+    ds = ds.rename(map="landcover")
+    ax = plot_esa_worldcover(ds)
+    assert ax is not None, "Expected a valid Matplotlib Axes object."
+    assert len(ax.images) > 0, "Expected at least one image in the plot."
+    ax.set_title("ESA WorldCover")