Linting

GlacioHack · Sep 2, 2023 · 8b08c0d · 8b08c0d
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Showing 5 changed files with 182 additions and 69 deletions.
diff --git a/tests/test_coreg/test_base.py b/tests/test_coreg/test_base.py
@@ -48,7 +48,7 @@ class TestCoregClass:
     # Create some 3D coordinates with Z coordinates being 0 to try the apply_pts functions.
     points = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [0, 0, 0, 0]], dtype="float64").T
 
-    def test_init(self):
+    def test_init(self) -> None:
         """Test instantiation of Coreg"""
 
         c = coreg.Coreg()
@@ -465,7 +465,14 @@ def test_pipeline(self) -> None:
         # Assert that the combined vertical shift is 2
         assert pipeline2.to_matrix()[2, 3] == 2.0
 
-    all_coregs = [coreg.VerticalShift(), coreg.NuthKaab(), coreg.ICP(), coreg.Deramp(), coreg.TerrainBias(), coreg.DirectionalBias()]
+    all_coregs = [
+        coreg.VerticalShift(),
+        coreg.NuthKaab(),
+        coreg.ICP(),
+        coreg.Deramp(),
+        coreg.TerrainBias(),
+        coreg.DirectionalBias(),
+    ]
 
     @pytest.mark.parametrize("coreg1", all_coregs)  # type: ignore
     @pytest.mark.parametrize("coreg2", all_coregs)  # type: ignore
@@ -479,12 +486,23 @@ def test_pipeline_combinations__nobiasvar(self, coreg1: Coreg, coreg2: Coreg) ->
         aligned_dem, _ = pipeline.apply(self.tba.data, transform=self.ref.transform, crs=self.ref.crs)
         assert aligned_dem.shape == self.ref.data.squeeze().shape
 
-    all_coregs = [coreg.VerticalShift(), coreg.NuthKaab(), coreg.ICP(), coreg.Deramp(), coreg.TerrainBias(),
-                  coreg.DirectionalBias()]
+    all_coregs = [
+        coreg.VerticalShift(),
+        coreg.NuthKaab(),
+        coreg.ICP(),
+        coreg.Deramp(),
+        coreg.TerrainBias(),
+        coreg.DirectionalBias(),
+    ]
 
     @pytest.mark.parametrize("coreg1", all_coregs)  # type: ignore
-    @pytest.mark.parametrize("coreg2", [coreg.BiasCorr1D(bias_var_names=["slope"], fit_or_bin="bin"),
-                                        coreg.BiasCorr2D(bias_var_names=["slope", "aspect"], fit_or_bin="bin")])  # type: ignore
+    @pytest.mark.parametrize(
+        "coreg2",
+        [
+            coreg.BiasCorr1D(bias_var_names=["slope"], fit_or_bin="bin"),
+            coreg.BiasCorr2D(bias_var_names=["slope", "aspect"], fit_or_bin="bin"),
+        ],
+    )  # type: ignore
     def test_pipeline_combinations__biasvar(self, coreg1: Coreg, coreg2: Coreg) -> None:
         """Test pipelines with all combinations of coregistration subclasses with bias variables"""
 
@@ -493,33 +511,54 @@ def test_pipeline_combinations__biasvar(self, coreg1: Coreg, coreg2: Coreg) -> N
         bias_vars = {"slope": xdem.terrain.slope(self.ref), "aspect": xdem.terrain.aspect(self.ref)}
         pipeline.fit(**self.fit_params, bias_vars=bias_vars)
 
-        aligned_dem, _ = pipeline.apply(self.tba.data, transform=self.ref.transform, crs=self.ref.crs, bias_vars=bias_vars)
+        aligned_dem, _ = pipeline.apply(
+            self.tba.data, transform=self.ref.transform, crs=self.ref.crs, bias_vars=bias_vars
+        )
         assert aligned_dem.shape == self.ref.data.squeeze().shape
 
-    def test_pipeline__errors(self):
+    def test_pipeline__errors(self) -> None:
         """Test pipeline raises proper errors."""
 
         pipeline = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr1D()])
-        with pytest.raises(ValueError, match=re.escape("No `bias_vars` passed to .fit() for bias correction step "
-                                             "<class 'xdem.coreg.biascorr.BiasCorr1D'> of the pipeline.")):
+        with pytest.raises(
+            ValueError,
+            match=re.escape(
+                "No `bias_vars` passed to .fit() for bias correction step "
+                "<class 'xdem.coreg.biascorr.BiasCorr1D'> of the pipeline."
+            ),
+        ):
             pipeline.fit(**self.fit_params)
 
-
         pipeline2 = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr1D(), coreg.BiasCorr1D()])
-        with pytest.raises(ValueError, match=re.escape("No `bias_vars` passed to .fit() for bias correction step <class 'xdem.coreg.biascorr.BiasCorr1D'> "
-                                             "of the pipeline. As you are using several bias correction steps requiring"
-                                             " `bias_vars`, don't forget to explicitly define their `bias_var_names` "
-                                             "during instantiation, e.g. BiasCorr1D(bias_var_names=['slope']).")):
+        with pytest.raises(
+            ValueError,
+            match=re.escape(
+                "No `bias_vars` passed to .fit() for bias correction step <class 'xdem.coreg.biascorr.BiasCorr1D'> "
+                "of the pipeline. As you are using several bias correction steps requiring"
+                " `bias_vars`, don't forget to explicitly define their `bias_var_names` "
+                "during instantiation, e.g. BiasCorr1D(bias_var_names=['slope'])."
+            ),
+        ):
             pipeline2.fit(**self.fit_params)
 
-        with pytest.raises(ValueError, match=re.escape("When using several bias correction steps requiring `bias_vars` in a pipeline," 
-                                             "the `bias_var_names` need to be explicitly defined at each step's "
-                                             "instantiation, e.g. BiasCorr1D(bias_var_names=['slope']).")):
+        with pytest.raises(
+            ValueError,
+            match=re.escape(
+                "When using several bias correction steps requiring `bias_vars` in a pipeline,"
+                "the `bias_var_names` need to be explicitly defined at each step's "
+                "instantiation, e.g. BiasCorr1D(bias_var_names=['slope'])."
+            ),
+        ):
             pipeline2.fit(**self.fit_params, bias_vars={"slope": xdem.terrain.slope(self.ref)})
 
         pipeline3 = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr1D(bias_var_names=["slope"])])
-        with pytest.raises(ValueError, match=re.escape("Not all keys of `bias_vars` in .fit() match the `bias_var_names` defined during "
-                                            "instantiation of the bias correction step <class 'xdem.coreg.biascorr.BiasCorr1D'>: ['slope'].")):
+        with pytest.raises(
+            ValueError,
+            match=re.escape(
+                "Not all keys of `bias_vars` in .fit() match the `bias_var_names` defined during "
+                "instantiation of the bias correction step <class 'xdem.coreg.biascorr.BiasCorr1D'>: ['slope']."
+            ),
+        ):
             pipeline3.fit(**self.fit_params, bias_vars={"ncc": xdem.terrain.slope(self.ref)})
 
     def test_pipeline_pts(self) -> None:

diff --git a/tests/test_coreg/test_biascorr.py b/tests/test_coreg/test_biascorr.py
@@ -87,7 +87,6 @@ def test_biascorr(self) -> None:
         bcorr5 = biascorr.BiasCorr(bias_var_names=np.array(["slope", "ncc"]))
         assert bcorr5._meta["bias_var_names"] == ["slope", "ncc"]
 
-
     def test_biascorr__errors(self) -> None:
         """Test the errors that should be raised by BiasCorr."""
 
@@ -354,8 +353,10 @@ def test_biascorr1d(self) -> None:
 
         # Raise error when variables don't match
         with pytest.raises(
-                ValueError, match=re.escape("The keys of `bias_vars` do not match the `bias_var_names` defined during "
-                                 "instantiation: ['ncc'].")
+            ValueError,
+            match=re.escape(
+                "The keys of `bias_vars` do not match the `bias_var_names` defined during " "instantiation: ['ncc']."
+            ),
         ):
             bcorr1d2 = biascorr.BiasCorr1D(bias_var_names=["ncc"])
             bias_vars_dict = {"elevation": self.ref}
@@ -391,8 +392,11 @@ def test_biascorr2d(self) -> None:
 
         # Raise error when variables don't match
         with pytest.raises(
-                ValueError, match=re.escape("The keys of `bias_vars` do not match the `bias_var_names` defined during "
-                                            "instantiation: ['elevation', 'ncc'].")
+            ValueError,
+            match=re.escape(
+                "The keys of `bias_vars` do not match the `bias_var_names` defined during "
+                "instantiation: ['elevation', 'ncc']."
+            ),
         ):
             bcorr2d2 = biascorr.BiasCorr2D(bias_var_names=["elevation", "ncc"])
             bias_vars_dict = {"elevation": self.ref, "slope": xdem.terrain.slope(self.ref)}
@@ -539,7 +543,6 @@ def test_terrainbias(self) -> None:
 
         assert tb._meta["bias_var_names"] == ["maximum_curvature"]
 
-
     def test_terrainbias__synthetic(self) -> None:
         """Test the subclass TerrainBias."""
 

diff --git a/xdem/coreg/affine.py b/xdem/coreg/affine.py
@@ -298,14 +298,20 @@ def _fit_func(
         transform: rio.transform.Affine,
         crs: rio.crs.CRS,
         weights: NDArrayf | None,
+        bias_vars: dict[str, NDArrayf] | None = None,
         verbose: bool = False,
         **kwargs: Any,
     ) -> None:
         # FOR DEVELOPERS: This function needs to be implemented.
         raise NotImplementedError("This step has to be implemented by subclassing.")
 
     def _apply_func(
-        self, dem: NDArrayf, transform: rio.transform.Affine, crs: rio.crs.CRS, **kwargs: Any
+        self,
+        dem: NDArrayf,
+        transform: rio.transform.Affine,
+        crs: rio.crs.CRS,
+        bias_vars: dict[str, NDArrayf] | None = None,
+        **kwargs: Any,
     ) -> tuple[NDArrayf, rio.transform.Affine]:
         # FOR DEVELOPERS: This function is only needed for non-rigid transforms.
         raise NotImplementedError("This should have been implemented by subclassing")
@@ -340,6 +346,7 @@ def _fit_func(
         transform: rio.transform.Affine,
         crs: rio.crs.CRS,
         weights: NDArrayf | None,
+        bias_vars: dict[str, NDArrayf] | None = None,
         verbose: bool = False,
         **kwargs: Any,
     ) -> None:
@@ -368,7 +375,12 @@ def _fit_func(
         self._meta["vshift"] = vshift
 
     def _apply_func(
-        self, dem: NDArrayf, transform: rio.transform.Affine, crs: rio.crs.CRS, **kwargs: Any
+        self,
+        dem: NDArrayf,
+        transform: rio.transform.Affine,
+        crs: rio.crs.CRS,
+        bias_vars: dict[str, NDArrayf] | None = None,
+        **kwargs: Any,
     ) -> tuple[NDArrayf, rio.transform.Affine]:
         """Apply the VerticalShift function to a DEM."""
         return dem + self._meta["vshift"], transform
@@ -426,6 +438,7 @@ def _fit_func(
         transform: rio.transform.Affine,
         crs: rio.crs.CRS,
         weights: NDArrayf | None,
+        bias_vars: dict[str, NDArrayf] | None = None,
         verbose: bool = False,
         **kwargs: Any,
     ) -> None:
@@ -575,6 +588,7 @@ def _fit_func(
         transform: rio.transform.Affine,
         crs: rio.crs.CRS,
         weights: NDArrayf | None,
+        bias_vars: dict[str, NDArrayf] | None = None,
         verbose: bool = False,
         **kwargs: Any,
     ) -> None:
@@ -589,7 +603,12 @@ def _fit_func(
         self._meta["func"] = fit_ramp
 
     def _apply_func(
-        self, dem: NDArrayf, transform: rio.transform.Affine, crs: rio.crs.CRS, **kwargs: Any
+        self,
+        dem: NDArrayf,
+        transform: rio.transform.Affine,
+        crs: rio.crs.CRS,
+        bias_vars: dict[str, NDArrayf] | None = None,
+        **kwargs: Any,
     ) -> tuple[NDArrayf, rio.transform.Affine]:
         """Apply the deramp function to a DEM."""
         x_coords, y_coords = _get_x_and_y_coords(dem.shape, transform)
@@ -652,6 +671,7 @@ def _fit_func(
         transform: rio.transform.Affine,
         crs: rio.crs.CRS,
         weights: NDArrayf | None,
+        bias_vars: dict[str, NDArrayf] | None = None,
         verbose: bool = False,
         **kwargs: Any,
     ) -> None:
@@ -928,7 +948,12 @@ def _to_matrix_func(self) -> NDArrayf:
         return matrix
 
     def _apply_func(
-        self, dem: NDArrayf, transform: rio.transform.Affine, crs: rio.crs.CRS, **kwargs: Any
+        self,
+        dem: NDArrayf,
+        transform: rio.transform.Affine,
+        crs: rio.crs.CRS,
+        bias_vars: dict[str, NDArrayf] | None = None,
+        **kwargs: Any,
     ) -> tuple[NDArrayf, rio.transform.Affine]:
         """Apply the Nuth & Kaab shift to a DEM."""
         offset_east = self._meta["offset_east_px"] * self._meta["resolution"]
@@ -1082,6 +1107,7 @@ def _fit_func(
         transform: rio.transform.Affine,
         crs: rio.crs.CRS,
         weights: NDArrayf | None,
+        bias_vars: dict[str, NDArrayf] | None = None,
         verbose: bool = False,
         **kwargs: Any,
     ) -> None: