Extend padding functionalities

doc/conf.py

@@ -153,6 +153,9 @@
     "matplotlib colormap name": ":doc:`matplotlib colormap name <matplotlib:gallery/color/colormap_reference>`",
     "matplotlib axes object": ":py:class:`matplotlib axes object <matplotlib.axes.Axes>`",
     "colormap": ":py:class:`colormap <matplotlib.colors.Colormap>`",
+    # xarray terms
+    "dim name": ":term:`dimension name <name>`",
+    "var name": ":term:`variable name <name>`",
     # objects without namespace: xarray
     "DataArray": "~xarray.DataArray",
     "Dataset": "~xarray.Dataset",

doc/whats-new.rst

@@ -24,6 +24,8 @@ New Features
 ~~~~~~~~~~~~
 - Make chunk manager an option in ``set_options`` (:pull:`9362`).
   By `Tom White <https://github.com/tomwhite>`_.
+- Allow data variable specific ``constant_values`` in the dataset ``pad`` function (:pull:`9353``).
+  By `Tiago Sanona <https://github.com/tsanona>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~

xarray/core/dataset.py

@@ -163,6 +163,7 @@
         ReindexMethodOptions,
         SideOptions,
         T_ChunkDimFreq,
+        T_DatasetPadConstantValues,
         T_Xarray,
     )
     from xarray.core.weighted import DatasetWeighted
@@ -9153,9 +9154,7 @@ def pad(
         stat_length: (
             int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None
         ) = None,
-        constant_values: (
-            float | tuple[float, float] | Mapping[Any, tuple[float, float]] | None
-        ) = None,
+        constant_values: T_DatasetPadConstantValues | None = None,
         end_values: int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None = None,
         reflect_type: PadReflectOptions = None,
         keep_attrs: bool | None = None,
@@ -9211,17 +9210,19 @@ def pad(
             (stat_length,) or int is a shortcut for before = after = statistic
             length for all axes.
             Default is ``None``, to use the entire axis.
-        constant_values : scalar, tuple or mapping of hashable to tuple, default: 0
-            Used in 'constant'.  The values to set the padded values for each
-            axis.
+        constant_values : scalar, tuple, mapping of dim name to scalar or tuple, or \
+            mapping of var name to scalar, tuple or to mapping of dim name to scalar or tuple, default: None
+            Used in 'constant'. The values to set the padded values for each data variable / axis.
+            ``{var_1: {dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}, ...
+            var_M: (before, after)}`` unique pad constants per data variable.
             ``{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}`` unique
             pad constants along each dimension.
             ``((before, after),)`` yields same before and after constants for each
             dimension.
             ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
             all dimensions.
-            Default is 0.
-        end_values : scalar, tuple or mapping of hashable to tuple, default: 0
+            Default is ``None``, pads with ``np.nan``.
+        end_values : scalar, tuple or mapping of hashable to tuple, default: None
             Used in 'linear_ramp'.  The values used for the ending value of the
             linear_ramp and that will form the edge of the padded array.
             ``{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}`` unique
@@ -9230,7 +9231,7 @@ def pad(
             axis.
             ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
             all axes.
-            Default is 0.
+            Default is None.
         reflect_type : {"even", "odd", None}, optional
             Used in "reflect", and "symmetric".  The "even" style is the
             default with an unaltered reflection around the edge value.  For
@@ -9304,11 +9305,22 @@ def pad(
             if not var_pad_width:
                 variables[name] = var
             elif name in self.data_vars:
+                if utils.is_dict_like(constant_values):
+                    if name in constant_values.keys():
+                        filtered_constant_values = constant_values[name]
+                    elif not set(var.dims).isdisjoint(constant_values.keys()):
+                        filtered_constant_values = {
+                            k: v for k, v in constant_values.items() if k in var.dims
+                        }
+                    else:
+                        filtered_constant_values = 0  # TODO: https://github.com/pydata/xarray/pull/9353#discussion_r1724018352
+                else:
+                    filtered_constant_values = constant_values
                 variables[name] = var.pad(
                     pad_width=var_pad_width,
                     mode=mode,
                     stat_length=stat_length,
-                    constant_values=constant_values,
+                    constant_values=filtered_constant_values,
                     end_values=end_values,
                     reflect_type=reflect_type,
                     keep_attrs=keep_attrs,

xarray/core/types.py

@@ -243,6 +243,11 @@ def copy(
     "symmetric",
     "wrap",
 ]
+T_PadConstantValues = float | tuple[float, float]
+T_VarPadConstantValues = T_PadConstantValues | Mapping[Any, T_PadConstantValues]
+T_DatasetPadConstantValues = (
+    T_VarPadConstantValues | Mapping[Any, T_VarPadConstantValues]
+)
 PadReflectOptions = Literal["even", "odd", None]
 
 CFCalendar = Literal[

xarray/core/variable.py

@@ -65,6 +65,7 @@
         Self,
         T_Chunks,
         T_DuckArray,
+        T_VarPadConstantValues,
     )
     from xarray.namedarray.parallelcompat import ChunkManagerEntrypoint
 
@@ -1121,9 +1122,14 @@ def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs):
 
     def _pad_options_dim_to_index(
         self,
-        pad_option: Mapping[Any, int | tuple[int, int]],
+        pad_option: Mapping[Any, int | float | tuple[int, int] | tuple[float, float]],
         fill_with_shape=False,
     ):
+        # change number values to a tuple of two of those values
+        for k, v in pad_option.items():
+            if isinstance(v, numbers.Number):
+                pad_option[k] = (v, v)
+
         if fill_with_shape:
             return [
                 (n, n) if d not in pad_option else pad_option[d]
@@ -1138,9 +1144,7 @@ def pad(
         stat_length: (
             int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None
         ) = None,
-        constant_values: (
-            float | tuple[float, float] | Mapping[Any, tuple[float, float]] | None
-        ) = None,
+        constant_values: T_VarPadConstantValues | None = None,
         end_values: int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None = None,
         reflect_type: PadReflectOptions = None,
         keep_attrs: bool | None = None,
@@ -1160,7 +1164,7 @@ def pad(
         stat_length : int, tuple or mapping of hashable to tuple
             Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
             values at edge of each axis used to calculate the statistic value.
-        constant_values : scalar, tuple or mapping of hashable to tuple
+        constant_values : scalar, tuple or mapping of hashable to scalar or tuple
             Used in 'constant'.  The values to set the padded values for each
             axis.
         end_values : scalar, tuple or mapping of hashable to tuple
@@ -1207,10 +1211,6 @@ def pad(
         if stat_length is None and mode in ["maximum", "mean", "median", "minimum"]:
             stat_length = [(n, n) for n in self.data.shape]  # type: ignore[assignment]
 
-        # change integer values to a tuple of two of those values and change pad_width to index
-        for k, v in pad_width.items():
-            if isinstance(v, numbers.Number):
-                pad_width[k] = (v, v)
         pad_width_by_index = self._pad_options_dim_to_index(pad_width)
 
         # create pad_options_kwargs, numpy/dask requires only relevant kwargs to be nonempty

xarray/tests/test_dataset.py

@@ -6704,18 +6704,80 @@ def test_polyfit_warnings(self) -> None:
             ds.var1.polyfit("dim2", 10, full=True)
             assert len(ws) == 1
 
-    def test_pad(self) -> None:
-        ds = create_test_data(seed=1)
-        padded = ds.pad(dim2=(1, 1), constant_values=42)
-
-        assert padded["dim2"].shape == (11,)
-        assert padded["var1"].shape == (8, 11)
-        assert padded["var2"].shape == (8, 11)
-        assert padded["var3"].shape == (10, 8)
-        assert dict(padded.sizes) == {"dim1": 8, "dim2": 11, "dim3": 10, "time": 20}
+    @staticmethod
+    def _test_data_var_interior(
+        original_data_var, padded_data_var, padded_dim_name, expected_pad_values
+    ):
+        np.testing.assert_equal(
+            np.unique(padded_data_var.isel({padded_dim_name: [0, -1]})),
+            expected_pad_values,
+        )
+        np.testing.assert_array_equal(
+            padded_data_var.isel({padded_dim_name: slice(1, -1)}), original_data_var
+        )
 
-        np.testing.assert_equal(padded["var1"].isel(dim2=[0, -1]).data, 42)
-        np.testing.assert_equal(padded["dim2"][[0, -1]].data, np.nan)
+    @pytest.mark.parametrize("padded_dim_name", ["dim1", "dim2", "dim3", "time"])
+    @pytest.mark.parametrize(
+        ["constant_values"],
+        [
+            pytest.param(None, id="default"),
+            pytest.param(42, id="scalar"),
+            pytest.param((42, 43), id="tuple"),
+            pytest.param({"dim1": 42, "dim2": 43}, id="per dim scalar"),
+            pytest.param({"dim1": (42, 43), "dim2": (43, 44)}, id="per dim tuple"),
+            pytest.param({"var1": 42, "var2": (42, 43)}, id="per var"),
+            pytest.param({"var1": 42, "dim1": (42, 43)}, id="mixed"),
+        ],
+    )
+    def test_pad(self, padded_dim_name, constant_values) -> None:
+        ds = create_test_data(seed=1)
+        padded = ds.pad({padded_dim_name: (1, 1)}, constant_values=constant_values)
+
+        # test padded dim values and size
+        for ds_dim_name, ds_dim in ds.sizes.items():
+            if ds_dim_name == padded_dim_name:
+                np.testing.assert_equal(padded.sizes[ds_dim_name], ds_dim + 2)
+                if ds_dim_name in padded.coords:
+                    assert padded[ds_dim_name][[0, -1]].isnull().all()
+            else:
+                np.testing.assert_equal(padded.sizes[ds_dim_name], ds_dim)
+
+        # check if coord "numbers" with dimention dim3 is paded correctly
+        if padded_dim_name == "dim3":
+            assert padded["numbers"][[0, -1]].isnull().all()
+            # twarning: passes but dtype changes from int to float
+            np.testing.assert_array_equal(padded["numbers"][1:-1], ds["numbers"])
+
+        # test if data_vars are paded with correct values
+        for data_var_name, data_var in padded.data_vars.items():
+            if padded_dim_name in data_var.dims:
+                if utils.is_dict_like(constant_values):
+                    if (
+                        expected := constant_values.get(data_var_name, None)
+                    ) is not None:
+                        self._test_data_var_interior(
+                            ds[data_var_name], data_var, padded_dim_name, expected
+                        )
+                    elif (
+                        expected := constant_values.get(padded_dim_name, None)
+                    ) is not None:
+                        self._test_data_var_interior(
+                            ds[data_var_name], data_var, padded_dim_name, expected
+                        )
+                    else:
+                        self._test_data_var_interior(
+                            ds[data_var_name], data_var, padded_dim_name, 0
+                        )
+                elif constant_values:
+                    self._test_data_var_interior(
+                        ds[data_var_name], data_var, padded_dim_name, constant_values
+                    )
+                else:
+                    self._test_data_var_interior(
+                        ds[data_var_name], data_var, padded_dim_name, np.nan
+                    )
+            else:
+                assert_array_equal(data_var, ds[data_var_name])
 
     @pytest.mark.parametrize(
         ["keep_attrs", "attrs", "expected"],