Dimensionless units as "1" - adapt to numpy 2

.github/workflows/main.yml

@@ -123,7 +123,7 @@ jobs:
  markers: -m 'not slow'
  os: windows-latest
  # macOS builds
- - tox-env: py310-coverage-extras-lmoments
+ - tox-env: py310-coverage-extras-lmoments-numpy
  python-version: "3.10"
  markers: -m 'not slow'
  os: macos-latest
@@ -135,7 +135,7 @@ jobs:
  - tox-env: py310-coverage-lmoments # No markers -- includes slow tests
  python-version: "3.10"
  os: ubuntu-latest
- - tox-env: py311-coverage-extras-sbck
+ - tox-env: py311-coverage-extras-sbck-numpy
  python-version: "3.11"
  markers: -m 'not slow'
  os: ubuntu-latest

CHANGELOG.rst

@@ -4,18 +4,23 @@ Changelog
 
 v0.52.0 (unreleased)
 --------------------
-Contributors to this version: David Huard (:user:`huard`), Trevor James Smith (:user:`Zeitsperre`), Hui-Min Wang (:user:`Hem-W`), Éric Dupuis (:user:`coxipi`), Sarah Gammon (:user:`SarahG-579462`).
+Contributors to this version: David Huard (:user:`huard`), Trevor James Smith (:user:`Zeitsperre`), Hui-Min Wang (:user:`Hem-W`), Éric Dupuis (:user:`coxipi`), Sarah Gammon (:user:`SarahG-579462`), Pascal Bourgault (:user:`aulemahal`).
 
 New features and enhancements
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 * ``xclim.sdba.nbutils.quantile`` and its child functions are now faster. If the module `fastnanquantile` is installed, it is used as the backend for the computation of quantiles and yields even faster results. This dependency is now listed in the `xclim[extras]` recipe. (:issue:`1255`, :pull:`1513`).
 * New multivariate bias adjustment class `MBCn`, giving a faster and more accurate implementation of the 'MBCn' algorithm (:issue:`1551`, :pull:`1580`).
 * `xclim` is now compatible with `pytest` versions `>=8.0.0`. (:pull:`1632`).
 
+Breaking changes
+^^^^^^^^^^^^^^^^
+* As updated in ``cf_xarray>=0.9.3``, dimensionless quantities now use the "1" units attribute as specified by the CF conventions, previously an empty string was returned. (:pull:`1814`).
+
 Bug fixes
 ^^^^^^^^^
 * Fixed the indexer bug in the ``xclim.indices.standardized_index_fit_params`` when multiple or non-array indexers are specified and fitted parameters are reloaded from netCDF. (:issue:`1842`, :pull:`1843`).
 * Addressed a bug found in ``wet_spell_*`` indicators that was contributing to erroneous results. A new generic spell length statistic function ``xclim.indices.generic.spell_length_statistics`` is now used in wet and dry spells indicators. (:issue:`1834`, :pull:`1838`).
+* Syntax for ``nan`` and ``inf`` was adapted to support ``numpy>=2.0.0``. (:pull:`1814`, :issue:`1785`).
 
 Internal changes
 ^^^^^^^^^^^^^^^^

docs/notebooks/sdba-advanced.ipynb

@@ -271,7 +271,9 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "QDM.ds.to_netcdf(\"QDM_training.nc\")\n",
+ "# The engine keyword is only needed if netCDF4 is not available\n",
+ "# The training dataset contains some attributes with types not supported by scipy/netCDF3\n",
+ "QDM.ds.to_netcdf(\"QDM_training.nc\", engine=\"h5netcdf\")\n",
  "ds = xr.open_dataset(\"QDM_training.nc\")\n",
  "QDM2 = QuantileDeltaMapping.from_dataset(ds)\n",
  "QDM2"
@@ -292,7 +294,7 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "QDM.ds.to_netcdf(\"QDM_training2.nc\")\n",
+ "QDM.ds.to_netcdf(\"QDM_training2.nc\", engine=\"h5netcdf\")\n",
  "ds = xr.open_dataset(\"QDM_training2.nc\")\n",
  "ds.attrs[\"title\"] = \"This is the dataset, but read from disk.\"\n",
  "QDM.set_dataset(ds)\n",
@@ -878,7 +880,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.11.4"
+ "version": "3.12.4"
  },
  "toc": {
  "base_numbering": 1,

docs/notebooks/sdba.ipynb

@@ -360,7 +360,7 @@
  "outputs": [],
  "source": [
  "# We are using xarray's \"air_temperature\" dataset\n",
- "ds = xr.tutorial.open_dataset(\"air_temperature\")"
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")"
  ]
  },
  {
@@ -598,7 +598,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "version": "3.12.4"
  },
  "toc": {
  "base_numbering": 1,

environment.yml

@@ -1,6 +1,5 @@
 name: xclim
 channels:
- - numba # Added to gain access to Python3.12-compatible numba release candidates.
  - conda-forge
  - defaults
 dependencies:
@@ -13,9 +12,9 @@ dependencies:
  - dask >=2.6.0
  - jsonpickle
  - numba
- - numpy >=1.20.0,<2.0.0
+ - numpy >=1.20.0
  - pandas >=2.2.0
- - pint >=0.10,<0.24
+ - pint >=0.18.0
  - poppler >=0.67
  - pyarrow # Strongly encouraged for Pandas v2.2.0+
  - pyyaml
@@ -53,7 +52,6 @@ dependencies:
  - nbsphinx
  - nbval >=0.11.0
  - nc-time-axis
- - netCDF4 >=1.4,<1.7
  - notebook
  - pandas-stubs
  - platformdirs

pyproject.toml

@@ -41,10 +41,10 @@ dependencies = [
  "filelock",
  "jsonpickle",
  "numba",
- "numpy>=1.20.0,<2.0.0",
+ "numpy>=1.20.0",
  "packaging",
  "pandas>=2.2",
- "pint>=0.10,<0.24",
+ "pint>=0.18",
  "platformdirs >=3.2",
  "pyarrow", # Strongly encouraged for pandas v2.2.0+
  "pyyaml",
@@ -74,7 +74,6 @@ dev = [
  "nbconvert <7.14", # Pinned due to directive errors in sphinx. See: https://github.com/jupyter/nbconvert/issues/2092
  "nbqa >=1.8.2",
  "nbval >=0.11.0",
- "netCDF4 >=1.4,<1.7",
  "pandas-stubs >=2.2",
  "platformdirs >=3.2",
  "pooch",
@@ -181,7 +180,7 @@ pep621_dev_dependency_groups = ["all", "dev", "docs"]
 
 [tool.deptry.per_rule_ignores]
 DEP001 = ["SBCK"]
-DEP002 = ["bottleneck", "pyarrow"]
+DEP002 = ["bottleneck", "h5netcdf", "pyarrow"]
 DEP004 = ["matplotlib", "pytest", "pytest_socket"]
 
 [tool.flit.sdist]

tests/test_atmos.py

@@ -4,6 +4,8 @@
 
 import numpy as np
 import xarray as xr
+from cf_xarray import __version__ as __cfxr_version__
+from packaging.version import Version
 
 from xclim import atmos, set_options
 
@@ -260,7 +262,11 @@ def test_wind_profile(atmosds):
 
 def test_wind_power_potential(atmosds):
  out = atmos.wind_power_potential(wind_speed=atmosds.sfcWind)
- assert out.attrs["units"] == ""
+
+ if Version(__cfxr_version__) < Version("0.9.3"):
+ assert out.attrs["units"] == ""
+ else:
+ assert out.attrs["units"] == "1"
  assert (out >= 0).all()
  assert (out <= 1).all()
 

tests/test_calendar.py

@@ -281,7 +281,7 @@ def test_convert_calendar_and_doy():
  out = convert_doy(doy, target_cal="360_day", align_on="date").convert_calendar(
  "360_day", align_on="date"
  )
- np.testing.assert_array_equal(out, [np.NaN, 31, 332, 360.23, np.NaN])
+ np.testing.assert_array_equal(out, [np.nan, 31, 332, 360.23, np.nan])
  assert out.time.dt.calendar == "360_day"
 
 
@@ -456,7 +456,7 @@ def test_convert_doy():
  )
 
  out = convert_doy(doy, "360_day", align_on="date")
- np.testing.assert_array_equal(out, [np.NaN, 31, 332, 360.23, np.NaN])
+ np.testing.assert_array_equal(out, [np.nan, 31, 332, 360.23, np.nan])
  assert out.time.dt.calendar == "noleap"
  out = convert_doy(doy, "360_day", align_on="year")
  np.testing.assert_allclose(
@@ -475,7 +475,7 @@ def test_convert_doy():
  ).expand_dims(lat=[10, 20, 30])
 
  out = convert_doy(doy, "noleap", align_on="date")
- np.testing.assert_array_equal(out.isel(lat=0), [31, 200.48, 190, np.NaN, 299.54])
+ np.testing.assert_array_equal(out.isel(lat=0), [31, 200.48, 190, np.nan, 299.54])
  out = convert_doy(doy, "noleap", align_on="year")
  np.testing.assert_allclose(
  out.isel(lat=0), [31.0, 200.48, 190.0, 59.83607, 299.71885]

tests/test_cli.py

@@ -89,7 +89,7 @@ def test_normal_computation(
  input_file = tmp_path / "in.nc"
  output_file = tmp_path / "out.nc"
 
- ds.to_netcdf(input_file)
+ ds.to_netcdf(input_file, engine="h5netcdf")
 
  args = ["-i", str(input_file), "-o", str(output_file), "-v", indicator]
  runner = CliRunner()
@@ -136,7 +136,7 @@ def test_multi_output(tmp_path, open_dataset):
  ds = open_dataset("ERA5/daily_surface_cancities_1990-1993.nc")
  input_file = tmp_path / "ws_in.nc"
  output_file = tmp_path / "out.nc"
- ds.to_netcdf(input_file)
+ ds.to_netcdf(input_file, engine="h5netcdf")
 
  runner = CliRunner()
  results = runner.invoke(
@@ -216,7 +216,7 @@ def test_missing_variable(tas_series, tmp_path):
  input_file = tmp_path / "tas.nc"
  output_file = tmp_path / "out.nc"
 
- tas.to_netcdf(input_file)
+ tas.to_netcdf(input_file, engine="h5netcdf")
 
  runner = CliRunner()
  results = runner.invoke(
@@ -243,7 +243,7 @@ def test_global_options(tas_series, tmp_path, options, output):
  input_file = tmp_path / "tas.nc"
  output_file = tmp_path / "out.nc"
 
- tas.to_netcdf(input_file)
+ tas.to_netcdf(input_file, engine="h5netcdf")
 
  runner = CliRunner()
  results = runner.invoke(
@@ -283,7 +283,7 @@ def test_dataflags_output(tmp_path, tas_series, tasmax_series, tasmin_series):
  arr = series(vals, start="1971-01-01")
  ds = xr.merge([ds, arr])
  input_file = tmp_path / "ws_in.nc"
- ds.to_netcdf(input_file)
+ ds.to_netcdf(input_file, engine="h5netcdf")
 
  runner = CliRunner()
  results = runner.invoke(
@@ -303,7 +303,7 @@ def test_bad_usage(tas_series, tmp_path):
  input_file = tmp_path / "tas.nc"
  output_file = tmp_path / "out.nc"
 
- tas.to_netcdf(input_file)
+ tas.to_netcdf(input_file, engine="h5netcdf")
 
  runner = CliRunner()
 

tests/test_ensembles.py

@@ -79,7 +79,7 @@ def test_no_time(self, tmp_path, ensemble_dataset_objects, open_dataset):
  ds["time"] = xr.decode_cf(ds).time
  ds_all.append(ds.groupby(ds.time.dt.month).mean("time", keep_attrs=True))
  ds.groupby(ds.time.dt.month).mean("time", keep_attrs=True).to_netcdf(
- f1.joinpath(Path(n).name)
+ f1.joinpath(Path(n).name), engine="h5netcdf"
  )
  ens = ensembles.create_ensemble(ds_all)
 
@@ -288,8 +288,14 @@ def test_calc_mean_std_min_max(self, ensemble_dataset_objects, open_dataset):
  )
  out2 = ensembles.ensemble_mean_std_max_min(ens, weights=weights)
  values = ens["tg_mean"][:, 0, 5, 5]
+ # Explicit float64 so numpy does the expected datatype promotion (change in numpy 2)
  np.testing.assert_array_equal(
- (values[0] * 1 + values[1] * 0.1 + values[2] * 3.5 + values[3] * 5)
+ (
+ values[0] * np.float64(1)
+ + values[1] * np.float64(0.1)
+ + values[2] * np.float64(3.5)
+ + values[3] * np.float64(5)
+ )
  / np.sum(weights),
  out2.tg_mean_mean[0, 5, 5],
  )
@@ -311,7 +317,7 @@ def test_stats_min_members(self, ensemble_dataset_objects, open_dataset, aggfunc
  ds_all = [open_dataset(n) for n in ensemble_dataset_objects["nc_files_simple"]]
  ens = ensembles.create_ensemble(ds_all).isel(lat=0, lon=0)
  ens = ens.where(ens.realization > 0)
- ens = xr.where((ens.realization == 1) & (ens.time.dt.year == 1950), np.NaN, ens)
+ ens = xr.where((ens.realization == 1) & (ens.time.dt.year == 1950), np.nan, ens)
 
  def first(ds):
  return ds[list(ds.data_vars.keys())[0]]

tests/test_generic.py

@@ -5,6 +5,8 @@
 import numpy as np
 import pytest
 import xarray as xr
+from cf_xarray import __version__ as __cfxr_version__
+from packaging.version import Version
 
 from xclim.core.calendar import doy_to_days_since, select_time
 from xclim.indices import generic
@@ -101,7 +103,11 @@ def test_doyminmax(self, q_series):
  for da in [dmx, dmn]:
  for attr in ["units", "is_dayofyear", "calendar"]:
  assert attr in da.attrs.keys()
- assert da.attrs["units"] == ""
+
+ if Version(__cfxr_version__) < Version("0.9.3"):
+ assert da.attrs["units"] == ""
+ else:
+ assert da.attrs["units"] == "1"
  assert da.attrs["is_dayofyear"] == 1
 
 
@@ -393,13 +399,13 @@ def test_generic_forbidden_op(self, pr_series):
 
 class TestGetDailyEvents:
  def test_simple(self, tas_series):
- arr = xr.DataArray(np.array([-10, 15, 20, np.NaN, 10]), name="Stuff")
+ arr = xr.DataArray(np.array([-10, 15, 20, np.nan, 10]), name="Stuff")
 
  out = generic.get_daily_events(arr, threshold=10, op=">=")
 
  assert out.name == "events"
  assert out.sum() == 3
- np.testing.assert_array_equal(out, [0, 1, 1, np.NaN, 1])
+ np.testing.assert_array_equal(out, [0, 1, 1, np.nan, 1])
 
 
 class TestGenericCountingIndices:
@@ -470,7 +476,7 @@ def test_count_occurrences(self, tas_series, op, constrain, expected, should_fai
  @pytest.mark.parametrize(
  "op, constrain, expected, should_fail",
  [
- ("<", None, np.NaN, False),
+ ("<", None, np.nan, False),
  ("<=", None, 3, False),
  ("!=", ("!=",), 1, False),
  ("==", ("==", "!="), 3, False),
@@ -497,7 +503,7 @@ def test_first_occurrence(self, tas_series, op, constrain, expected, should_fail
  @pytest.mark.parametrize(
  "op, constrain, expected, should_fail",
  [
- ("<", None, np.NaN, False),
+ ("<", None, np.nan, False),
  ("<=", None, 8, False),
  ("!=", ("!=",), 9, False),
  ("==", ("==", "!="), 8, False),

tests/test_hydrology.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import numpy as np
+from cf_xarray import __version__ as __cfxr_version__
+from packaging.version import Version
 
 from xclim import indices as xci
 
@@ -40,7 +42,11 @@ def test_simple(self, snw_series):
  snw = snw_series(a, start="1999-01-01")
  out = xci.snw_max_doy(snw, freq="YS")
  np.testing.assert_array_equal(out, [11, np.nan])
- assert out.units == ""
+
+ if Version(__cfxr_version__) < Version("0.9.3"):
+ assert out.attrs["units"] == ""
+ else:
+ assert out.attrs["units"] == "1"
 
 
 class TestSnowMeltWEMax:

tests/test_indicators.py

@@ -848,7 +848,7 @@ def test_resampling_indicator_with_indexing(tas_series):
  out = xclim.atmos.tx_days_above(
  tas, thresh="0 degC", freq="YS-JUL", doy_bounds=(1, 50)
  )
- np.testing.assert_allclose(out, [50, 50, np.NaN])
+ np.testing.assert_allclose(out, [50, 50, np.nan])
 
  out = xclim.atmos.tx_days_above(
  tas, thresh="0 degC", freq="YS", date_bounds=("02-29", "04-01")