Name kriging singular-fallback variance '{name}_variance'

.claude/sweep-api-consistency-state.csv

@@ -2,6 +2,7 @@ module,last_inspected,issue,severity_max,categories_found,notes
 focal,2026-06-10,3215;3216,MEDIUM,3;4,"Sweep 2026-06-10 (deep-sweep-api-consistency-focal-2026-06-10). 2 MEDIUM findings filed, fixed on branches -01/-02 off this one. (#3215, MEDIUM Cat 4 cross-backend default parity, branch -01) apply() default func=_calc_mean is an @ngjit CPU function but the cupy/dask+cupy paths launch func as a CUDA kernel via _focal_stats_func_cupy func[griddim, blockdim], so apply(cupy_agg, kernel) raises TypeError 'CPUDispatcher' object is not subscriptable (dask+cupy builds the graph and fails at compute). Prior 2026-05-29 sweep dispositioned this LOW as 'documented in the docstring', but the docstring covers explicit funcs -- the default itself is unusable on 2 of 4 backends. Fix: func=None sentinel resolved per backend (_calc_mean CPU, _focal_mean_cuda GPU), explicit-func behavior unchanged; same PR adds the missing name= param to the apply() docstring (signature has name='focal_apply'; mean/focal_stats/hotspots document theirs). (#3216, MEDIUM Cat 3, branch -02) hotspots() docstring lists 3 backends but dask_cupy_func=_hotspots_dask_cupy is dispatched and works; kernel param documented as binary ('values of 1 indicate the kernel') while hotspots accepts weighted kernels and the Gi* formula in the same docstring uses weights w_ij (apply/focal_stats reject non-binary via _validate_binary_kernel, hotspots deliberately does not). Docs-only fix. LOW documented, not fixed: among the 4 focal publics only mean() has @supports_dataset (Dataset-support drift; feature gap, not an API bug). Cross-cutting, notes only per template: emerging_hotspots(raster=), viewshed(raster=), calc_cellsize(raster) still use raster while focal standardized on agg with a DeprecationWarning shim (#2689/PR #2699); library-wide first-arg drift, belongs to those modules' sweeps. No Cat 1 in-module (agg canonical, raster alias warns, both-args raises). No Cat 2 return drift (mean/apply/hotspots 2D same-type, focal_stats 3D (stats,y,x) as documented). No Cat 5 orphan API (apply/focal_stats/hotspots documented in focal.rst autosummary and consumed via xrspatial.focal module path; only mean re-exported top-level; emerging_hotspots top-level vs hotspots module-level asymmetry noted, additive export would be a design call, not filed). cuda-validated: CUDA_AVAILABLE=True on this host; mean/apply/focal_stats/hotspots smoke-tested on cupy with kwarg parity; the apply default crash reproduced on GPU; hotspots weighted-kernel acceptance verified empirically."
 geotiff,2026-06-12,3263;3265,MEDIUM,3;5,"Re-sweep 2026-06-12 (deep-sweep-api-consistency-geotiff-2026-06-12); prior pass 2026-06-09 (#3086). Scope: surface changes since 2026-06-09 (pack/unpack fixes #3171-#3241, SUPPORTED_FEATURES reader.unpack/writer.pack/reader.coregister, coregister docs #3248) plus a fresh 5-category pass on open_geotiff/to_geotiff. 2 MEDIUM findings filed and fixed on branches -01/-02 off this one. (#3263, MEDIUM Cat 3, PR #3269, branch -01) open_geotiff unpack docstring said 'A source without scale / offset metadata is a no-op', but unpack=True folds into the masking gate (_finalize_eager_read: mask_and_scale implies masking, rioxarray parity), so a sentinel-bearing uint16 source still comes back float64 with NaN holes; verified identical on all 4 backends (not a parity bug), only a source with neither scale/offset nor a sentinel reads unchanged. Docs-only fix + test_unpack_noop_doc_3263.py pinning wording (scoped to the unpack paragraph) and behavior. (#3265, MEDIUM Cat 5, PR #3273, branch -02) exception-export drift: VRTUnsupportedError (raised 10+ times in _vrt_validation.py on public .vrt reads, documented in geotiff_safe_io.rst which steered users to the private _errors module), CloudSizeLimitError (importable but not in __all__, sibling UnsafeURLError IS exported), and PixelSafetyLimitError (raised by the [stable] max_pixels cap, only importable from _layout/_reader) were the only 3 exceptions raised on public open_geotiff paths missing from the public surface (other 17 exported). Additive fix: import + __all__ + :class: roles in safe_io doc + trigger-point docs naming the exceptions in max_pixels/max_cloud_bytes param docs and geotiff.rst; test_exception_exports_3265.py pins export, identity with private definitions, and a functional max_pixels raise. Clean elsewhere: docstring/signature parity exact on both publics (programmatic check + 218 existing contract tests); no Cat 1 (signatures unchanged since 2026-06-09; pack/unpack pair deliberate), no Cat 2 (DataArray / path returns unchanged), no Cat 4 (shared allow_* defaults match reader/writer; gpu False-vs-None auto-detect documented). SUPPORTED_FEATURES tiers (reader.unpack/writer.pack/reader.coregister experimental) agree with docstring tier markers. coregister= itself lives on accessor.py (excluded module) -- only its SUPPORTED_FEATURES registration is in geotiff, consistent. cuda-validated: CUDA_AVAILABLE=True; open_geotiff smoke-tested with identical kwargs on numpy/cupy/dask/dask+cupy (cpu/gpu pixel parity), to_geotiff gpu=True, cupy pack=True write (#3240 fix confirmed), deprecated aliases mask_and_scale/name/mask_nodata all warn. Both PRs reviewed (COMMENTED) with findings fixed in follow-up commits c14844a8/af3c8a66; branches up to date with origin/main; left for user merge per REVIEW_REQUIRED."
 hydro-d8,2026-05-29,2709,HIGH,1;5,"Sweep 2026-05-29 (deep-sweep-api-consistency-hydro-d8-2026-05-29). Scope = the 13 D8-variant files only; dinf/mfd read for reference but not modified. 1 HIGH Cat 1 + 1 MEDIUM Cat 5 fixed in this branch (#2709, PR #2716). HIGH Cat 1: stream_order_d8 named its strahler/shreve selector `ordering` while sibling stream_order_dinf/stream_order_mfd use `method`; both names live in the public API and the __init__.py _StreamOrderDispatch special-cases the drift (translates ordering->method for non-d8). Fix adds `method` as an accepted alias on stream_order_d8 (case-insensitive; takes precedence; conflicting ordering+method raises ValueError), keeping `ordering` working so the out-of-scope dispatcher (passes ordering=) and existing callers are unaffected. Full rename to `method` deferred because deprecating `ordering` would warn on every stream_order(routing='d8') call via the dispatcher I cannot touch in this scope. MEDIUM Cat 5: basins_d8 (watershed_d8.py) is a backward-compat wrapper whose docstring said 'use basin instead' but emitted no warning; added DeprecationWarning(stacklevel=2). Tests added for alias parity/precedence/conflict/case-insensitivity and for the basins_d8 warning. Findings documented but NOT filed per template: (LOW Cat 1 cross-module, out of scope) dinf siblings name the first arg `flow_dir_dinf` (stream_link/flow_path/hand/watershed_dinf) while all D8 funcs use the cleaner `flow_dir`; D8 is the better convention so no D8 change -- the drift lives in the dinf files. (LOW Cat 4 defensive-validation drift) hand_d8 validates np.isfinite(threshold) but stream_link_d8/stream_order_d8 (same threshold: float = 100 param) do not; not user-facing signature surprise, document only. No Cat 2 return drift (every D8 public fn returns xr.DataArray with coords/dims/attrs preserved; Dataset in -> Dataset out via @supports_dataset). No Cat 3 missing-hints beyond fill_d8 z_limit (optional, no hint) which mirrors its sibling style. All 13 D8 funcs are re-exported in xrspatial/hydro/__init__.py (no orphan API). cuda-validated: CUDA_AVAILABLE=True on this host; method-alias parity smoke-tested on a cupy DataArray. CI: ubuntu/windows/3.12 GitHub Actions green; macOS-3.14 + ReadTheDocs slow but no failures. NOTE: the /review-pr review comment could not be posted to GitHub (auto-mode permission denial on gh pr review); review findings were applied to code instead (case-insensitive conflict check + str|None hint, commit f8467320)."
+interpolate,2026-06-12,3285,MEDIUM,2,"Sweep 2026-06-12 (deep-sweep-api-consistency-interpolate-2026-06-12). Scope: idw/_idw.py, kriging/_kriging.py, spline/_spline.py, shared _validation.py. 1 MEDIUM Cat 2 finding filed as #3285, fixed on branch -01 off this one: kriging(return_variance=True) singular-matrix fallback (_kriging.py:499) returns prediction, prediction.copy() so the variance DataArray keeps the prediction's name instead of f'{name}_variance' (normal path :523 names it correctly); reproduced by monkeypatching _build_kriging_matrix to None; anything keying on .name (xr.merge, Dataset build) silently collapses the pair. One-line fix + regression test on the singular path. Clean elsewhere: Cat 1 in-module exact (idw/kriging/spline share x, y, z, template positionals and name= default '<func>'; template matches kde's template=); docstring/signature parity exact on all 3 publics (every param documented, Returns sections match incl. kriging's tuple); Cat 4 no default drift (power=2.0, k=None, fill_value=nan, variogram_model='spherical', nlags=15, smoothing=0.0, all single-owner params); Cat 5 no orphan API (all 3 re-exported in xrspatial/__init__.py and autosummaried in docs/source/reference/interpolation.rst; tests touch private helpers only via module paths). Cross-cutting, notes only per template: fill_value (idw) vs fill (rasterize) for the uncovered-pixel value is library-wide drift (idw matches numpy's fill_value convention, left alone); public functions are untyped module-wide (consistent internally, drifts from typed kde/rasterize/proximity siblings -- annotation pass would span the whole module, LOW, not filed); kde's keyword-only style is the library minority so interpolate's positional style matches the rasterize/proximity majority. GPU k-nearest rejection (NotImplementedError) is deliberate and documented in the k param docstring. cuda-validated: CUDA_AVAILABLE=True on this host; idw/kriging/spline smoke-tested with full kwargs on numpy AND cupy DataArrays (variance name parity confirmed on both), dask+numpy and dask+cupy graph construction verified without compute."
 mcda,2026-06-10,3148,HIGH,1;2;3;5,"Sweep 2026-06-10 (deep-sweep-api-consistency-mcda-2026-06-10). Fixed in this branch (#3148): (HIGH Cat 1) owa() named its criterion-weight dict criterion_weights while wlc/wpm/sensitivity use weights (same semantics, same _validate_weights); renamed to weights with keyword-only criterion_weights deprecation shim (DeprecationWarning; both names -> TypeError; positional callers untouched). (MEDIUM Cat 2) boolean_overlay annotated criteria as dict-only while every sibling combiner takes xr.Dataset; Dataset already worked via the Mapping interface -- now annotated/documented as xr.Dataset | dict. (MEDIUM Cat 3) ahp_weights docstring Raises claimed ValueError on incomplete comparisons but code warns (UserWarning) and defaults missing pairs to 1 -- docstring now documents Warns behaviour. (MEDIUM Cat 5) ConsistencyResult returned by public ahp_weights but absent from xrspatial/mcda __all__ and docs/source/reference/mcda.rst -- exported and documented. Documented, NOT fixed here: (MEDIUM Cat 2, deferred to parallel sweep-metadata sibling to avoid duplicate PR) constrain() drops attrs via xr.where while the other nine public functions preserve them. (LOW Cat 2) ahp_weights returns (weights, ConsistencyResult) tuple vs rank_weights bare dict -- intentional, documented in both docstrings, no fix. (LOW Cat 4) name=None inherit-input-name (standardize/constrain) vs literal-name defaults (combiners) -- defensible split, document only. Pre-existing backend bugs surfaced by the mandated cupy smoke (accuracy/test-coverage lane, recorded in #3148 body): owa fails on cupy (numpy order-weights array mixed into cupy multiply, combine.py ~336-340) and on ANY dask backend at graph construction (da.sort does not exist, combine.py:356, despite the owa MemoryError message recommending dask); sensitivity(method=monte_carlo) fails on cupy (template.values implicit-conversion guard). constrain on cupy blocked by the known library-wide cupy 13.6 + xarray xr.where astype incompat (dependency-pin issue), not mcda-specific. cuda-validated: CUDA_AVAILABLE=True; all 10 public functions smoke-tested on cupy DataArrays; owa weights=/criterion_weights= shim verified on numpy AND cupy entry points (cupy execution stops at the pre-existing mixed-array bug, signature acceptance confirmed)."
 polygonize,2026-05-19,2148,HIGH,1;3,"Sweep 2026-05-19 (deep-sweep-api-consistency-polygonize-2026-05-19). 1 MEDIUM Cat 3 finding fixed in this branch (#2148): polygonize() was the only public vector/raster conversion function without a return type annotation. Sieve/contours/rasterize/clip_polygon all declare one. Fix adds a Union return annotation (numpy tuple | awkward tuple | geopandas GeoDataFrame | spatialpandas GeoDataFrame | geojson dict) using TYPE_CHECKING forward refs for optional deps, and expands the docstring Returns section to enumerate the per-return_type shapes. 1 HIGH Cat 1 finding NOT fixed in this PR -- cross-module rename: polygonize uses `connectivity` (int 4|8) while sieve uses `neighborhood` (int 4|8) for the identical rook/queen pixel-connectivity concept. Industry convention (GDAL, rasterio.features.sieve) favours `connectivity`; the deprecation shim belongs in sieve.py, not polygonize, so this is out of scope for the polygonize-scoped sweep branch. Documented here for the next sieve sweep pass. 1 LOW Cat 1 cross-cutting: polygonize/sieve/clip_polygon use `raster` while contours and many older modules use `agg` for the input DataArray -- library-wide drift, not filed per-module per sweep template. Cat 2 return-shape: polygonize returns tuple/GeoDataFrame/dict by return_type; consistent with contours' tuple/GeoDataFrame dispatch. No Cat 4 (no mutable defaults; connectivity=4 default matches sieve neighborhood=4 default). No Cat 5 (polygonize re-exported in xrspatial/__init__.py; no orphan API; no __all__ but consistent with module convention). cuda-validated: cupy backend accepts identical kwargs, smoke-tested with cupy DataArray on host with CUDA_AVAILABLE."
 proximity,2026-06-09,3090;3091,HIGH,2;3,"Sweep 2026-06-09 (deep-sweep-api-consistency-proximity-2026-06-09). 1 HIGH Cat 2 finding (#3090): dask+numpy (and unbounded dask+cupy, which converts to it) KDTree path violates the documented lowest-flat-index tie-break in allocation()/direction() whenever the raster has >1 chunk column. _collect_region_targets concatenates targets chunk-major (iy outer, ix inner) so the tree's target order is not global row-major; _kdtree_query_lowest_index then ties to the wrong target. Existing tie-break tests put both targets in the same raster row where chunk order coincides with row-major, so they pass. Repro: 5x5, targets 2@(1,3) and 3@(2,2), chunks (5,3), pixel (2,3) tied at d=1 -> numpy gives 2, dask gives 3. Bounded map_overlap paths are fine (local row-major order is offset-invariant). 1 MEDIUM Cat 3 finding (#3091): all 3 public docstrings claim numpy + dask+numpy support only while cupy/dask+cupy backends exist, are dispatched, and are tested (the tie-break paragraphs in the same docstrings name all 4 backends); direction() opens with a stray copy-pasted slope line ('downward slope direction') plus a doubled 'the the'; allocation example output reads as float64 but the function returns float32; stale '# convert to have same type as of input @raster' comment. Within-module Cat 1/4/5 clean: proximity/allocation/direction share an identical signature (raster, x='x', y='y', target_values=None, max_distance=np.inf, distance_metric='EUCLIDEAN'); consistent with surface_distance siblings (raster/x/y/target_values/max_distance); all 6 public symbols (incl. euclidean/manhattan/great_circle_distance) re-exported in __init__.py, no orphan API. Cross-cutting, documented not filed: sibling distance modules (surface_distance, cost_distance, balanced_allocation) use mutable default target_values: list = [] while proximity uses the None sentinel - the mutable-default fix belongs to those modules; proximity's target_values: list = None hint would be more precise as Optional[list] (LOW, matches library style). cuda-validated: CUDA_AVAILABLE=True on this host; proximity/allocation/direction smoke-tested with identical kwargs on numpy, cupy, dask+numpy, dask+cupy (proximity parity passed; allocation/direction parity failure is finding #3090)."

xrspatial/interpolate/_kriging.py

@@ -496,7 +496,9 @@ def vario_func(h):
             attrs=template.attrs,
         )
         if return_variance:
-            return prediction, prediction.copy()
+            variance = prediction.copy()
+            variance.name = f'{name}_variance'
+            return prediction, variance
         return prediction
 
     mapper = ArrayTypeFunctionMapping(

xrspatial/tests/test_interpolation.py

@@ -420,6 +420,31 @@ def test_return_variance(self):
         assert isinstance(pred, xr.DataArray)
         assert isinstance(var, xr.DataArray)
         assert pred.shape == var.shape
+        assert pred.name == 'kriging'
+        assert var.name == 'kriging_variance'
+
+    def test_return_variance_name_on_singular_fallback(self, monkeypatch):
+        """Singular-matrix fallback names the variance '{name}_variance'.
+
+        Regression test for #3285: the fallback path returned
+        prediction.copy(), which kept the prediction's name, so both
+        arrays came back with the same name and anything keying on
+        .name (e.g. xr.merge) collapsed the pair.
+        """
+        monkeypatch.setattr(
+            'xrspatial.interpolate._kriging._build_kriging_matrix',
+            lambda *args, **kwargs: None,
+        )
+
+        x, y, z = self._spatial_data()
+        template = _make_template([0.0, 2.0, 4.0], [0.0, 2.0, 4.0])
+        pred, var = kriging(x, y, z, template, return_variance=True,
+                            name='krig')
+
+        assert pred.name == 'krig'
+        assert var.name == 'krig_variance'
+        assert np.all(np.isnan(pred.values))
+        assert np.all(np.isnan(var.values))
 
     def test_variogram_models(self):
         """All three variogram models should produce finite output."""