Issue #1437 fix recharge from imod5 cap data 2d (#1448)

JoerivanEngelen · web-flow · commit b537f7f54c75 · 2025-02-25T11:33:22.000Z
Fixes #1437 # Description ``primod`` expects a 2D rate array or single layer array for the dummy MODFLOW6 package, as otherwise the ``squeeze("layer")`` it does returns an error. I think the safest for iMOD Python is to return a 2D array with extra layer coordinate. I added some extra tests to test if this RCH package could be clipped and regridded, as in most/all fixtures for clipping/regridding I added packages with layer dimension. Luckily these two extra tests turned out to be mostly for my own peace of mind, as they ran without problems. # Checklist - [x] Links to correct issue - [x] Update changelog, if changes affect users - [x] PR title starts with ``Issue #nr``, e.g. ``Issue #737`` - [x] Unit tests were added - [ ] **If feature added**: Added/extended example
diff --git a/docs/api/changelog.rst b/docs/api/changelog.rst
@@ -91,6 +91,9 @@ Fixed
   like MetaSWAP expects.
 - Models imported with :meth:`imod.msw.MetaSwapModel.from_imod5_data` can be
   written with ``validate`` set to True.
+- :meth:`imod.mf6.Recharge.from_imod5_cap_data` now returns a 2D array with a
+  ``"layer"`` coordinate of ``1`` as otherwise ``primod`` throws an error when
+  trying to derive recharge-svat mappings.
 - Fixed part of the code that made Pandas, Geopandas, and xarray throw a lot of
   ``FutureWarning``s and ``DeprecationWarning``s.
 - Fixed performance issue when coverting very large wells (>10k) with
diff --git a/imod/mf6/rch.py b/imod/mf6/rch.py
@@ -279,11 +279,8 @@ def from_imod5_cap_data(
         active = msw_active.all
 
         data = {}
-        layer_da = xr.full_like(
-            target_dis.dataset.coords["layer"], np.nan, dtype=np.float64
-        )
-        layer_da.loc[{"layer": 1}] = 0.0
-        data["rate"] = layer_da.where(active)
+        zero_scalar = xr.DataArray(0.0, coords={"layer": 1})
+        data["rate"] = zero_scalar.where(active)
 
         return cls(**data, validate=True, fixed_cell=False)
 
diff --git a/imod/tests/test_mf6/test_mf6_rch.py b/imod/tests/test_mf6/test_mf6_rch.py
@@ -10,6 +10,7 @@
 
 import imod
 from imod.mf6.dis import StructuredDiscretization
+from imod.mf6.utilities.regrid import RegridderWeightsCache
 from imod.mf6.write_context import WriteContext
 from imod.schemata import ValidationError
 from imod.typing.grid import is_planar_grid, is_transient_data_grid, nan_like
@@ -467,18 +468,72 @@ def test_from_imod5_cap_data(imod5_dataset):
     data["cap"]["boundary"] = msw_bound
     data["cap"]["wetted_area"] = xr.ones_like(msw_bound) * 100
     data["cap"]["urban_area"] = xr.ones_like(msw_bound) * 200
+    # Compute midpoint of grid and set areas such, that cells need to be
+    # deactivated.
+    midpoint = tuple((int(x / 2) for x in msw_bound.shape))
     # Set to total cellsize, cell needs to be deactivated.
-    data["cap"]["wetted_area"][100, 100] = 625.0
+    data["cap"]["wetted_area"][midpoint] = 625.0
     # Set to zero, cell needs to be deactivated.
-    data["cap"]["urban_area"][100, 100] = 0.0
+    data["cap"]["urban_area"][midpoint] = 0.0
     # Act
     rch = imod.mf6.Recharge.from_imod5_cap_data(data, target_discretization)
     rate = rch.dataset["rate"]
     # Assert
+    # Shape
+    assert rate.dims == ("y", "x")
+    assert "layer" in rate.coords
+    assert rate.coords["layer"] == 1
+    # Values
     np.testing.assert_array_equal(np.unique(rate), np.array([0.0, np.nan]))
     # Boundaries inactive in MetaSWAP
-    assert np.isnan(rate[0, :, 0]).all()
-    assert np.isnan(rate[0, :, -1]).all()
-    assert np.isnan(rate[0, 0, :]).all()
-    assert np.isnan(rate[0, -1, :]).all()
-    assert np.isnan(rate[:, 100, 100]).all()
+    assert np.isnan(rate[:, 0]).all()
+    assert np.isnan(rate[:, -1]).all()
+    assert np.isnan(rate[0, :]).all()
+    assert np.isnan(rate[-1, :]).all()
+    assert np.isnan(rate[midpoint]).all()
+
+
+@pytest.mark.unittest_jit
+def test_from_imod5_cap_data__regrid(imod5_dataset):
+    # Arrange
+    data = deepcopy(imod5_dataset[0])
+    target_discretization = StructuredDiscretization.from_imod5_data(data)
+    data["cap"] = {}
+    msw_bound = data["bnd"]["ibound"].isel(layer=0, drop=False)
+    data["cap"]["boundary"] = msw_bound
+    data["cap"]["wetted_area"] = xr.ones_like(msw_bound) * 100
+    data["cap"]["urban_area"] = xr.ones_like(msw_bound) * 200
+    # Setup template grid
+    dx_small, xmin, xmax, dy_small, ymin, ymax = imod.util.spatial_reference(msw_bound)
+    dx = dx_small * 2
+    dy = dy_small * 2
+    expected_spatial_ref = dx, xmin, xmax, dy, ymin, ymax
+    like = imod.util.empty_2d(*expected_spatial_ref)
+    # Act
+    rch = imod.mf6.Recharge.from_imod5_cap_data(data, target_discretization)
+    rch_coarse = rch.regrid_like(like, regrid_cache=RegridderWeightsCache())
+    # Assert
+    actual_spatial_ref = imod.util.spatial_reference(rch_coarse.dataset["rate"])
+    assert actual_spatial_ref == expected_spatial_ref
+
+
+@pytest.mark.unittest_jit
+def test_from_imod5_cap_data__clip_box(imod5_dataset):
+    # Arrange
+    data = deepcopy(imod5_dataset[0])
+    target_discretization = StructuredDiscretization.from_imod5_data(data)
+    data["cap"] = {}
+    msw_bound = data["bnd"]["ibound"].isel(layer=0, drop=False)
+    data["cap"]["boundary"] = msw_bound
+    data["cap"]["wetted_area"] = xr.ones_like(msw_bound) * 100
+    data["cap"]["urban_area"] = xr.ones_like(msw_bound) * 200
+    # Setup template grid
+    dx, xmin, xmax, dy, ymin, ymax = imod.util.spatial_reference(msw_bound)
+    xmin_to_clip = xmin + 10 * dx
+    expected_spatial_ref = dx, xmin_to_clip, xmax, dy, ymin, ymax
+    # Act
+    rch = imod.mf6.Recharge.from_imod5_cap_data(data, target_discretization)
+    rch_clipped = rch.clip_box(x_min=xmin_to_clip)
+    # Assert
+    actual_spatial_ref = imod.util.spatial_reference(rch_clipped.dataset["rate"])
+    assert actual_spatial_ref == expected_spatial_ref
