Fix some warnings raised in the tests with xarray 2023.09.0

xarray now explicitly complains if you're doing Bad Things, instead of
silently trying to work with it. Explicit warnings are good! But now
I have to not do those Bad Things.

This has the upside of removing one of the warnings the tests silenced,
which can only be a good thing.

Author: mx-moth

tests/conventions/test_cfgrid1d.py

@@ -86,6 +86,10 @@ def make_dataset(
     # EMS fails to parse. There is no way around this using xarray natively,
     # you have to adjust it with nctool after saving it.
     time.encoding["units"] = "days since 1990-01-01 00:00:00 +10"
+    # This can not be represented as an int, so explicitly set the dtype.
+    # xarray >=2023.09 warns when encoding variables without a dtype
+    # that can not be represented exactly.
+    time.encoding["dtype"] = "float32"
 
     botz = xarray.DataArray(
         data=numpy.random.random((height, width)) * 10 + 50,

tests/conventions/test_cfgrid2d.py

@@ -94,6 +94,14 @@ def make_dataset(
             "coordinate_type": "time",
         },
     )
+    # Note: xarray will reformat this in to 1990-01-01T00:00:00+10:00, which
+    # EMS fails to parse. There is no way around this using xarray natively,
+    # you have to adjust it with nctool after saving it.
+    time.encoding["units"] = "days since 1990-01-01 00:00:00 +10"
+    # This can not be represented as an int, so explicitly set the dtype.
+    # xarray >=2023.09 warns when encoding variables without a dtype
+    # that can not be represented exactly.
+    time.encoding["dtype"] = "float32"
 
     botz = xarray.DataArray(
         data=numpy.random.random((j_size, i_size)) * 10 + 50,

tests/conventions/test_shoc_standard.py

@@ -98,6 +98,10 @@ def make_dataset(
     # EMS fails to parse. There is no way around this using xarray natively,
     # you have to adjust it with nctool after saving it.
     t.encoding["units"] = "days since 1990-01-01 00:00:00 +10"
+    # This can not be represented as an int, so explicitly set the dtype.
+    # xarray >=2023.09 warns when encoding variables without a dtype
+    # that can not be represented exactly.
+    t.encoding["dtype"] = "float32"
 
     botz = xarray.DataArray(
         data=numpy.random.random((j_size, i_size)) * 10 + 50,

tests/conventions/test_ugrid.py

@@ -206,6 +206,10 @@ def make_dataset(
     # EMS fails to parse. There is no way around this using xarray natively,
     # you have to adjust it with nctool after saving it.
     t.encoding["units"] = "days since 1990-01-01 00:00:00 +10"
+    # This can not be represented as an int, so explicitly set the dtype.
+    # xarray >=2023.09 warns when encoding variables without a dtype
+    # that can not be represented exactly.
+    t.encoding["dtype"] = "float32"
 
     botz = xarray.DataArray(
         data=numpy.random.random(cell_size) * 10 + 50,

tests/masking/test_mask_dataset.py

@@ -136,6 +136,7 @@ def test_mask_dataset(tmp_path: pathlib.Path):
         },
     )
     t.encoding["units"] = "days since 1990-01-01 00:00:00 +10"
+    t.encoding["dtype"] = numpy.dtype("float32")
 
     botz_missing_value = numpy.float32(-99.)
     botz = xarray.DataArray(

tests/test_utils.py

@@ -1,6 +1,7 @@
 import datetime
 import logging
 import pathlib
+from importlib.metadata import version
 
 import netCDF4
 import numpy
@@ -9,13 +10,17 @@
 import pytest
 import xarray
 import xarray.testing
+from packaging.version import parse
 
 from emsarray import utils
 from tests.utils import filter_warning
 
 logger = logging.getLogger(__name__)
 
 
+xarray_version = parse(version('xarray'))
+
+
 @pytest.mark.parametrize(
     ('existing', 'new'),
     [
@@ -87,6 +92,7 @@ def test_disable_default_fill_value(tmp_path: pathlib.Path):
         data=td_data, dims=['j', 'i'])
     timedelta_with_missing_value_var.encoding['missing_value'] = numpy.float64('1e35')
     timedelta_with_missing_value_var.encoding['units'] = 'days'
+    timedelta_with_missing_value_var.encoding['dtype'] = numpy.dtype('float64')
 
     dataset = xarray.Dataset(data_vars={
         "int_var": int_var,
@@ -96,7 +102,7 @@ def test_disable_default_fill_value(tmp_path: pathlib.Path):
     })
 
     # Save to a netCDF4 and then prove that it is bad
-    # This emits warnings in current versions of xarray / numpy.
+    # This emits warnings in older versions of xarray / numpy.
     # See https://github.com/pydata/xarray/issues/7942
     with filter_warning(
         'default', category=RuntimeWarning,
@@ -105,7 +111,10 @@ def test_disable_default_fill_value(tmp_path: pathlib.Path):
         record=True,
     ) as ws:
         dataset.to_netcdf(tmp_path / "bad.nc")
-        assert len(ws) == 1
+        if xarray_version >= parse('2023.09'):
+            assert len(ws) == 0
+        else:
+            assert len(ws) == 1
 
     with netCDF4.Dataset(tmp_path / "bad.nc", "r") as nc_dataset:
         # This one shouldn't be here because it is an integer datatype. xarray
@@ -116,6 +125,8 @@ def test_disable_default_fill_value(tmp_path: pathlib.Path):
         assert numpy.isnan(nc_dataset.variables["float_var"].getncattr("_FillValue"))
         # This one is quite alright, we did explicitly set it after all
         assert numpy.isnan(nc_dataset.variables["float_with_fill_value_var"].getncattr("_FillValue"))
+        # Same with this one
+        assert nc_dataset.variables["timedelta_with_missing_value_var"].getncattr("missing_value") == numpy.float64('1e35')
         # This one is incorrect, a `missing_value` attribute has already been set
         assert numpy.isnan(nc_dataset.variables["timedelta_with_missing_value_var"].getncattr("_FillValue"))
 
@@ -129,7 +140,10 @@ def test_disable_default_fill_value(tmp_path: pathlib.Path):
         record=True,
     ) as ws:
         dataset.to_netcdf(tmp_path / "good.nc")
-        assert len(ws) == 1
+        if xarray_version >= parse('2023.09'):
+            assert len(ws) == 0
+        else:
+            assert len(ws) == 1
 
     with netCDF4.Dataset(tmp_path / "good.nc", "r") as nc_dataset:
         # This one should still be unset