Merge 6f5ca2b into 6caeb19

doc/source/api.md

@@ -244,17 +244,6 @@ To build and pass your coregistration pipeline to {func}`~xdem.DEM.coregister_3d
     xdem.coreg.BiasCorr
 ```
 
-**Classes for any 1-, 2- and N-D biases:**
-
-```{eval-rst}
-.. autosummary::
-    :toctree: gen_modules/
-
-    xdem.coreg.BiasCorr1D
-    xdem.coreg.BiasCorr2D
-    xdem.coreg.BiasCorrND
-```
-
 **Convenience classes for specific corrections:**
 
 ```{eval-rst}

doc/source/biascorr.md

@@ -158,16 +158,3 @@ terbias.fit(ref_dem, tba_dem, inlier_mask=inlier_mask)
 # Apply the transformation
 corrected_dem = terbias.apply(tba_dem)
 ```
-
-## Generic 1-D, 2-D and N-D classes
-
-All bias-corrections methods are inherited from generic classes that perform corrections in 1-, 2- or N-D. Having these
-separate helps the user navigating the dimensionality of the functions, optimizer, binning or variables used.
-
-{class}`xdem.coreg.BiasCorr1D`
-{class}`xdem.coreg.BiasCorr2D`
-{class}`xdem.coreg.BiasCorrND`
-
-- **Performs:** Correct biases with any function and optimizer, or any binning, in 1-, 2- or N-D.
-- **Supports weights** Yes.
-- **Recommended for:** Anything.

tests/test_coreg/test_base.py

@@ -568,8 +568,8 @@ def test_pipeline_combinations__nobiasvar(self, coreg1: Coreg, coreg2: Coreg) ->
     @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"),
+            coreg.BiasCorr(bias_var_names=["slope"], fit_or_bin="bin"),
+            coreg.BiasCorr(bias_var_names=["slope", "aspect"], fit_or_bin="bin"),
         ],
     )  # type: ignore
     def test_pipeline_combinations__biasvar(self, coreg1: Coreg, coreg2: Coreg) -> None:
@@ -588,24 +588,24 @@ def test_pipeline_combinations__biasvar(self, coreg1: Coreg, coreg2: Coreg) -> N
     def test_pipeline__errors(self) -> None:
         """Test pipeline raises proper errors."""
 
-        pipeline = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr1D()])
+        pipeline = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr()])
         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."
+                "<class 'xdem.coreg.biascorr.BiasCorr'> of the pipeline."
             ),
         ):
             pipeline.fit(**self.fit_params)
 
-        pipeline2 = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr1D(), coreg.BiasCorr1D()])
+        pipeline2 = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr(), coreg.BiasCorr()])
         with pytest.raises(
             ValueError,
             match=re.escape(
-                "No `bias_vars` passed to .fit() for bias correction step <class 'xdem.coreg.biascorr.BiasCorr1D'> "
+                "No `bias_vars` passed to .fit() for bias correction step <class 'xdem.coreg.biascorr.BiasCorr'> "
                 "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'])."
+                "during instantiation, e.g. BiasCorr(bias_var_names=['slope'])."
             ),
         ):
             pipeline2.fit(**self.fit_params)
@@ -615,17 +615,17 @@ def test_pipeline__errors(self) -> None:
             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'])."
+                "instantiation, e.g. BiasCorr(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"])])
+        pipeline3 = coreg.CoregPipeline([coreg.NuthKaab(), coreg.BiasCorr(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']."
+                "instantiation of the bias correction step <class 'xdem.coreg.biascorr.BiasCorr'>: ['slope']."
             ),
         ):
             pipeline3.fit(**self.fit_params, bias_vars={"ncc": xdem.terrain.slope(self.ref)})

tests/test_coreg/test_biascorr.py

@@ -156,6 +156,48 @@ def test_biascorr__errors(self) -> None:
         ):
             biascorr.BiasCorr(fit_or_bin="bin", bin_apply_method=1)  # type: ignore
 
+        # When wrong number of parameters are passed
+
+        # Copy fit parameters
+        fit_args = self.fit_args_rst_rst.copy()
+        with pytest.raises(
+            ValueError,
+            match=re.escape("A number of 1 variable(s) has to be provided through the argument 'bias_vars', " "got 2."),
+        ):
+            bias_vars_dict = {"elevation": self.ref, "slope": xdem.terrain.slope(self.ref)}
+            bcorr1d = biascorr.BiasCorr(bias_var_names=["elevation"])
+            bcorr1d.fit(**fit_args, bias_vars=bias_vars_dict)
+
+        with pytest.raises(
+            ValueError,
+            match=re.escape("A number of 2 variable(s) has to be provided through the argument " "'bias_vars', got 1."),
+        ):
+            bias_vars_dict = {"elevation": self.ref}
+            bcorr2d = biascorr.BiasCorr(bias_var_names=["elevation", "slope"])
+            bcorr2d.fit(**fit_args, bias_vars=bias_vars_dict)
+
+        # 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']."
+            ),
+        ):
+            bcorr1d2 = biascorr.BiasCorr(bias_var_names=["ncc"])
+            bias_vars_dict = {"elevation": self.ref}
+            bcorr1d2.fit(**fit_args, bias_vars=bias_vars_dict)
+
+        with pytest.raises(
+            ValueError,
+            match=re.escape(
+                "The keys of `bias_vars` do not match the `bias_var_names` defined during "
+                "instantiation: ['elevation', 'ncc']."
+            ),
+        ):
+            bcorr2d2 = biascorr.BiasCorr(bias_var_names=["elevation", "ncc"])
+            bias_vars_dict = {"elevation": self.ref, "slope": xdem.terrain.slope(self.ref)}
+            bcorr2d2.fit(**fit_args, bias_vars=bias_vars_dict)
+
     @pytest.mark.parametrize("fit_args", all_fit_args)  # type: ignore
     @pytest.mark.parametrize(
         "fit_func", ("norder_polynomial", "nfreq_sumsin", lambda x, a, b: x[0] * a + b)
@@ -354,87 +396,6 @@ def test_biascorr__bin_and_fit_2d(self, fit_args, fit_func, fit_optimizer, bin_s
         # Apply the correction
         bcorr.apply(elev=self.tba, bias_vars=bias_vars_dict)
 
-    @pytest.mark.parametrize("fit_args", [fit_args_rst_pts, fit_args_rst_rst])  # type: ignore
-    def test_biascorr1d(self, fit_args) -> None:
-        """
-        Test the subclass BiasCorr1D, which defines default parameters for 1D.
-        The rest is already tested in test_biascorr.
-        """
-
-        # Try default "fit" parameters instantiation
-        bcorr1d = biascorr.BiasCorr1D()
-
-        assert bcorr1d._meta["fit_func"] == biascorr.fit_workflows["norder_polynomial"]["func"]
-        assert bcorr1d._meta["fit_optimizer"] == biascorr.fit_workflows["norder_polynomial"]["optimizer"]
-        assert bcorr1d._needs_vars is True
-
-        # Try default "bin" parameter instantiation
-        bcorr1d = biascorr.BiasCorr1D(fit_or_bin="bin")
-
-        assert bcorr1d._meta["bin_sizes"] == 10
-        assert bcorr1d._meta["bin_statistic"] == np.nanmedian
-        assert bcorr1d._meta["bin_apply_method"] == "linear"
-
-        elev_fit_args = fit_args.copy()
-        # Raise error when wrong number of parameters are passed
-        with pytest.raises(
-            ValueError, match="A single variable has to be provided through the argument 'bias_vars', " "got 2."
-        ):
-            bias_vars_dict = {"elevation": self.ref, "slope": xdem.terrain.slope(self.ref)}
-            bcorr1d.fit(**elev_fit_args, bias_vars=bias_vars_dict)
-
-        # 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']."
-            ),
-        ):
-            bcorr1d2 = biascorr.BiasCorr1D(bias_var_names=["ncc"])
-            bias_vars_dict = {"elevation": self.ref}
-            bcorr1d2.fit(**elev_fit_args, bias_vars=bias_vars_dict)
-
-    @pytest.mark.parametrize("fit_args", all_fit_args)  # type: ignore
-    def test_biascorr2d(self, fit_args) -> None:
-        """
-        Test the subclass BiasCorr2D, which defines default parameters for 2D.
-        The rest is already tested in test_biascorr.
-        """
-
-        # Try default "fit" parameters instantiation
-        bcorr2d = biascorr.BiasCorr2D()
-
-        assert bcorr2d._meta["fit_func"] == polynomial_2d
-        assert bcorr2d._meta["fit_optimizer"] == scipy.optimize.curve_fit
-        assert bcorr2d._needs_vars is True
-
-        # Try default "bin" parameter instantiation
-        bcorr2d = biascorr.BiasCorr2D(fit_or_bin="bin")
-
-        assert bcorr2d._meta["bin_sizes"] == 10
-        assert bcorr2d._meta["bin_statistic"] == np.nanmedian
-        assert bcorr2d._meta["bin_apply_method"] == "linear"
-
-        elev_fit_args = fit_args.copy()
-        # Raise error when wrong number of parameters are passed
-        with pytest.raises(
-            ValueError, match="Exactly two variables have to be provided through the argument " "'bias_vars', got 1."
-        ):
-            bias_vars_dict = {"elevation": self.ref}
-            bcorr2d.fit(**elev_fit_args, bias_vars=bias_vars_dict)
-
-        # 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']."
-            ),
-        ):
-            bcorr2d2 = biascorr.BiasCorr2D(bias_var_names=["elevation", "ncc"])
-            bias_vars_dict = {"elevation": self.ref, "slope": xdem.terrain.slope(self.ref)}
-            bcorr2d2.fit(**elev_fit_args, bias_vars=bias_vars_dict)
-
     def test_directionalbias(self) -> None:
         """Test the subclass DirectionalBias."""
 

xdem/coreg/__init__.py

@@ -17,13 +17,5 @@
     apply_matrix,
     invert_matrix,
 )
-from xdem.coreg.biascorr import (  # noqa
-    BiasCorr,
-    BiasCorr1D,
-    BiasCorr2D,
-    BiasCorrND,
-    Deramp,
-    DirectionalBias,
-    TerrainBias,
-)
+from xdem.coreg.biascorr import BiasCorr, Deramp, DirectionalBias, TerrainBias  # noqa
 from xdem.coreg.workflows import dem_coregistration  # noqa

xdem/coreg/base.py

@@ -1026,6 +1026,7 @@ class CoregDict(TypedDict, total=False):
     bin_statistic: Callable[[NDArrayf], np.floating[Any]]
     bin_apply_method: Literal["linear"] | Literal["per_bin"]
     bias_var_names: list[str]
+    nd: int | None
 
     # 2/ Outputs
     fit_params: NDArrayf