numpy 2 compatibility in the netcdf4 and h5netcdf backends (#9136)

* don't remove `netcdf4` from the upstream-dev environment

* also stop removing `h5py` and `hdf5`

* hard-code the precision (I believe this was missed in #9081)

* don't remove `h5py` either

* use on-diks _FillValue as standrd expects, use view instead of cast to prevent OverflowError.

* whats-new

* unpin `numpy`

* rework UnsignedCoder

* add test

* Update xarray/coding/variables.py

Co-authored-by: Justus Magin <keewis@users.noreply.github.com>

---------

Co-authored-by: Kai Mühlbauer <kai.muehlbauer@uni-bonn.de>
Co-authored-by: Kai Mühlbauer <kmuehlbauer@wradlib.org>
Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

ci/install-upstream-wheels.sh

@@ -13,7 +13,7 @@ $conda remove -y numba numbagg sparse
 # temporarily remove numexpr
 $conda remove -y numexpr
 # temporarily remove backends
-$conda remove -y cf_units hdf5 h5py netcdf4 pydap
+$conda remove -y cf_units pydap
 # forcibly remove packages to avoid artifacts
 $conda remove -y --force \
     numpy \
@@ -37,8 +37,7 @@ python -m pip install \
     numpy \
     scipy \
     matplotlib \
-    pandas \
-    h5py
+    pandas
 # for some reason pandas depends on pyarrow already.
 # Remove once a `pyarrow` version compiled with `numpy>=2.0` is on `conda-forge`
 python -m pip install \

ci/requirements/all-but-dask.yml

@@ -22,7 +22,7 @@ dependencies:
   - netcdf4
   - numba
   - numbagg
-  - numpy<2
+  - numpy
   - packaging
   - pandas
   - pint>=0.22

ci/requirements/environment-windows.yml

@@ -23,7 +23,7 @@ dependencies:
   - netcdf4
   - numba
   - numbagg
-  - numpy<2
+  - numpy
   - packaging
   - pandas
   # - pint>=0.22

ci/requirements/environment.yml

@@ -26,7 +26,7 @@ dependencies:
   - numba
   - numbagg
   - numexpr
-  - numpy<2
+  - numpy
   - opt_einsum
   - packaging
   - pandas

doc/whats-new.rst

@@ -45,6 +45,8 @@ Bug fixes
   By `Pontus Lurcock <https://github.com/pont-us>`_.
 - Allow diffing objects with array attributes on variables (:issue:`9153`, :pull:`9169`).
   By `Justus Magin <https://github.com/keewis>`_.
+- ``numpy>=2`` compatibility in the ``netcdf4`` backend (:pull:`9136`).
+  By `Justus Magin <https://github.com/keewis>`_ and `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
 - Promote floating-point numeric datetimes before decoding (:issue:`9179`, :pull:`9182`).
   By `Justus Magin <https://github.com/keewis>`_.
 - Address regression introduced in :pull:`9002` that prevented objects returned
@@ -67,7 +69,7 @@ Documentation
 - Adds a flow-chart diagram to help users navigate help resources (`Discussion #8990 <https://github.com/pydata/xarray/discussions/8990>`_).
   By `Jessica Scheick <https://github.com/jessicas11>`_.
 - Improvements to Zarr & chunking docs (:pull:`9139`, :pull:`9140`, :pull:`9132`)
-  By `Maximilian Roos <https://github.com/max-sixty>`_
+  By `Maximilian Roos <https://github.com/max-sixty>`_.
 
 
 Internal Changes

xarray/coding/variables.py

@@ -516,10 +516,13 @@ def encode(self, variable: Variable, name: T_Name = None) -> Variable:
             dims, data, attrs, encoding = unpack_for_encoding(variable)
 
             pop_to(encoding, attrs, "_Unsigned")
-            signed_dtype = np.dtype(f"i{data.dtype.itemsize}")
+            # we need the on-disk type here
+            # trying to get it from encoding, resort to an int with the same precision as data.dtype if not available
+            signed_dtype = np.dtype(encoding.get("dtype", f"i{data.dtype.itemsize}"))
             if "_FillValue" in attrs:
-                new_fill = signed_dtype.type(attrs["_FillValue"])
-                attrs["_FillValue"] = new_fill
+                new_fill = np.array(attrs["_FillValue"])
+                # use view here to prevent OverflowError
+                attrs["_FillValue"] = new_fill.view(signed_dtype).item()
             data = duck_array_ops.astype(duck_array_ops.around(data), signed_dtype)
 
             return Variable(dims, data, attrs, encoding, fastpath=True)
@@ -535,10 +538,11 @@ def decode(self, variable: Variable, name: T_Name = None) -> Variable:
                 if unsigned == "true":
                     unsigned_dtype = np.dtype(f"u{data.dtype.itemsize}")
                     transform = partial(np.asarray, dtype=unsigned_dtype)
-                    data = lazy_elemwise_func(data, transform, unsigned_dtype)
                     if "_FillValue" in attrs:
-                        new_fill = unsigned_dtype.type(attrs["_FillValue"])
-                        attrs["_FillValue"] = new_fill
+                        new_fill = np.array(attrs["_FillValue"], dtype=data.dtype)
+                        # use view here to prevent OverflowError
+                        attrs["_FillValue"] = new_fill.view(unsigned_dtype).item()
+                    data = lazy_elemwise_func(data, transform, unsigned_dtype)
             elif data.dtype.kind == "u":
                 if unsigned == "false":
                     signed_dtype = np.dtype(f"i{data.dtype.itemsize}")

xarray/tests/test_backends.py

@@ -166,7 +166,7 @@ def create_encoded_masked_and_scaled_data(dtype: np.dtype) -> Dataset:
 
 def create_unsigned_masked_scaled_data(dtype: np.dtype) -> Dataset:
     encoding = {
-        "_FillValue": 255,
+        "_FillValue": np.int8(-1),
         "_Unsigned": "true",
         "dtype": "i1",
         "add_offset": dtype.type(10),
@@ -925,6 +925,35 @@ def test_roundtrip_mask_and_scale(self, decoded_fn, encoded_fn, dtype) -> None:
                 assert decoded.variables[k].dtype == actual.variables[k].dtype
             assert_allclose(decoded, actual, decode_bytes=False)
 
+    @pytest.mark.parametrize("fillvalue", [np.int8(-1), np.uint8(255)])
+    def test_roundtrip_unsigned(self, fillvalue):
+        # regression/numpy2 test for
+        encoding = {
+            "_FillValue": fillvalue,
+            "_Unsigned": "true",
+            "dtype": "i1",
+        }
+        x = np.array([0, 1, 127, 128, 254, np.nan], dtype=np.float32)
+        decoded = Dataset({"x": ("t", x, {}, encoding)})
+
+        attributes = {
+            "_FillValue": fillvalue,
+            "_Unsigned": "true",
+        }
+        # Create unsigned data corresponding to [0, 1, 127, 128, 255] unsigned
+        sb = np.asarray([0, 1, 127, -128, -2, -1], dtype="i1")
+        encoded = Dataset({"x": ("t", sb, attributes)})
+
+        with self.roundtrip(decoded) as actual:
+            for k in decoded.variables:
+                assert decoded.variables[k].dtype == actual.variables[k].dtype
+            assert_allclose(decoded, actual, decode_bytes=False)
+
+        with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual:
+            for k in encoded.variables:
+                assert encoded.variables[k].dtype == actual.variables[k].dtype
+            assert_allclose(encoded, actual, decode_bytes=False)
+
     @staticmethod
     def _create_cf_dataset():
         original = Dataset(
@@ -4285,7 +4314,7 @@ def test_roundtrip_coordinates_with_space(self) -> None:
     def test_roundtrip_numpy_datetime_data(self) -> None:
         # Override method in DatasetIOBase - remove not applicable
         # save_kwargs
-        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
+        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"], unit="ns")
         expected = Dataset({"t": ("t", times), "t0": times[0]})
         with self.roundtrip(expected) as actual:
             assert_identical(expected, actual)