Merge pull request #116 from csiro-coasts/xarray-2023-09-fixes

Fix some warnings raised in the tests with xarray 2023.09.0

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