Adopt Pytest v9.0+, RuntimeWarning changes

CHANGELOG.rst

@@ -9,6 +9,7 @@ Contributors to this version: Éric Dupuis (:user:`coxipi`), Trevor James Smith
 Breaking changes
 ^^^^^^^^^^^^^^^^
 * `lmoments3` is now listed as a dependency to the extras recipe. This dependency is not installed by default with `xclim` and must be explicitly requested with ``$ pip install "xclim[extras]"``, if desired. (:pull:`2269`).
+* `numpy`-related `RuntimeWarnings` for invalid operations are noisier when running calculations via the ``xclim.indices`` and muted by default for ``xclim.indicators``. This change is made to ensure that users who perform indice calculations can be made more aware of potential inconsistencies in their source datasets (:issue:`2277`, :pull:`2276`).
 
 Bug fixes
 ^^^^^^^^^
@@ -18,6 +19,9 @@ Internal changes
 ^^^^^^^^^^^^^^^^
 * Replaced the ``tox.ini`` file with a ``tox.toml`` file and simplified the conditionals for environment selection. (:pull:`2269`).
 * Removed `python-coveralls` from the `tox`-only dependencies (abandoned software / not supported for Python 3.13 or 3.14) and added the `coverallsapp/github-action` step to PyPI/tox builds on CI. (:pull:`2269`).
+* The testing suite has been updated to support `pytest >=9.0` (:pull:`2276`):
+    * The configuration in `tox.toml` now uses the new TOML conventions.
+    * `--strict-config` and `--strict-markers` have been replaced with the new `--strict` mode. For more information, refer to the `pytest documentation <https://docs.pytest.org/en/stable/reference/reference.html#confval-strict>`_.
 
 v0.59.1 (2025-10-31)
 --------------------

environment.yml

@@ -34,8 +34,8 @@ dependencies:
   - coverage >=7.5.0
   - deptry >=0.23.0
   - distributed >=2.0
-  - flake8 >=7.2.0
-  - flake8-rst-docstrings >=0.3.1
+  - flake8 >=7.3.0
+  - flake8-rst-docstrings >=0.4.0
   - flit >=3.11.0,<4.0
   - furo >=2023.9.10
   - h5netcdf >=1.5.0
@@ -57,7 +57,7 @@ dependencies:
   - pre-commit >=3.7
   - pybtex >=0.24.0
   - pylint >=3.3.1
-  - pytest >=8.0.0
+  - pytest >=9.0.0
   - pytest-cov >=5.0.0
   - pytest-socket >=0.6.0
   - pytest-timeout >=2.4.0
@@ -72,10 +72,10 @@ dependencies:
   - sphinxcontrib-bibtex
   - sphinxcontrib-svg2pdfconverter
   - tokenize-rt >=5.2.0
-  - tox >=4.25.0
+  - tox >=4.31.0
   - tox-gh >=1.5.0
   - vulture =2.14
-  - xdoctest >=1.1.5
+  - xdoctest >=1.3.0
   - yamllint >=1.35.1
   # Temporary Fixes
   - importlib-metadata <8.7.0  # Issues with dask >=2025.5.1 on Python3.11

pyproject.toml

@@ -61,8 +61,8 @@ dev = [
   "codespell >=2.4.1",
   "coverage[toml] >=7.5.0",
   "deptry >=0.23.0",
-  "flake8 >=7.2.0",
-  "flake8-rst-docstrings >=0.3.1",
+  "flake8 >=7.3.0",
+  "flake8-rst-docstrings >=0.4.0",
   "h5netcdf >=1.5.0",
   "h5py >=3.12.1",
   "ipython >=8.10.0",
@@ -75,17 +75,17 @@ dev = [
   "pooch >=1.8.0",
   "pre-commit >=3.7",
   "pylint >=3.3.1",
-  "pytest >=8.0.0",
+  "pytest >=9.0.0",
   "pytest-cov >=5.0.0",
   "pytest-socket >=0.6.0",
   "pytest-timeout >=2.4.0",
   "pytest-xdist[psutil] >=3.2",
   "ruff >=0.13.3",
   "tokenize-rt >=5.2.0",
-  "tox >=4.25.0",
+  "tox >=4.31.0",
   "tox-gh >=1.5.0",
   "vulture >=2.14",
-  "xdoctest >=1.1.5",
+  "xdoctest >=1.3.0",
   "yamllint >=1.35.1"
 ]
 docs = [
@@ -263,34 +263,33 @@ override_SS05 = [
   '^Statistics '
 ]
 
-[tool.pytest.ini_options]
-minversion = "7.0"
+[tool.pytest]
+minversion = "9.0"
 addopts = [
   "-ra",
   "--color=yes",
   "--numprocesses=0",
   "--maxprocesses=8",
   "--dist=worksteal",
-  "--strict-config",
-  "--strict-markers"
-]
-log_cli_level = "INFO"
-norecursedirs = ["docs/notebooks/*"]
-filterwarnings = ["ignore::UserWarning"]
-testpaths = [
-  "tests"
-]
-pythonpath = [
-  "src"
+  "--strict"
 ]
 doctest_optionflags = ["NORMALIZE_WHITESPACE", "IGNORE_EXCEPTION_DETAIL", "NUMBER", "ELLIPSIS"]
+filterwarnings = ["ignore::UserWarning"]
+log_cli_level = "INFO"
 markers = [
   "slow: marks tests as slow (deselect with '-m \"not slow\"')",
   "requires_docs: mark tests that can only be run with documentation present (deselect with '-m \"not requires_docs\"')",
   "requires_internet: mark tests that require internet access (deselect with '-m \"not requires_internet\"')"
 ]
-session_timeout = 900
-timeout = 300
+norecursedirs = ["docs/notebooks/*"]
+pythonpath = [
+  "src"
+]
+session_timeout = "900" # must be string representing seconds (this may soon change)
+testpaths = [
+  "tests"
+]
+timeout = "300" # must be string representing seconds (this may soon change)
 timeout_method = "thread"
 xfail_strict = true
 

src/xclim/analog.py

@@ -579,8 +579,7 @@ def kldiv(x: np.ndarray, y: np.ndarray, *, k: int | Sequence[int] = 1) -> float
         # The 0th nearest neighbour of x[i] in x is x[i] itself.
         # Hence, we take the k'th + 1, which in 0-based indexing is given by
         # index k.
-        with np.errstate(divide="ignore"):
-            ki_calc = -np.log(r[:, ki] / s[:, ki - 1]).sum() * d / nx + np.log(ny / (nx - 1.0))
+        ki_calc = -np.log(r[:, ki] / s[:, ki - 1]).sum() * d / nx + np.log(ny / (nx - 1.0))
         out.append(ki_calc)
 
     if mk:

src/xclim/cli.py

@@ -436,6 +436,7 @@ def cli(ctx, **kwargs):  # numpydoc ignore=PR01
     if not kwargs["verbose"]:
         warnings.simplefilter("ignore", FutureWarning)
         warnings.simplefilter("ignore", DeprecationWarning)
+        warnings.simplefilter("ignore", RuntimeWarning)
 
     if kwargs["version"]:
         click.echo(f"xclim {xc.__version__}")

src/xclim/core/indicator.py

@@ -894,7 +894,7 @@ def __call__(self, *args, **kwds):
 
         # get mappings where keys are the actual compute function's argument names
         args = self._get_compute_args(das, params)
-        with xarray.set_options(keep_attrs=False):
+        with xarray.set_options(keep_attrs=False), np.errstate(divide="ignore", invalid="ignore"):
             outs = self.compute(**args)
 
         if isinstance(outs, DataArray):

src/xclim/ensembles/_base.py

@@ -130,7 +130,7 @@ def create_ensemble(
 
     dim = xr.IndexVariable("realization", list(realizations), attrs={"axis": "E"})
 
-    ens = xr.concat(ds, dim)
+    ens = xr.concat(ds, dim, compat="no_conflicts", join="outer")
     for var_name, var in ds[0].variables.items():
         ens[var_name].attrs.update(**var.attrs)
     ens.attrs.update(**ds[0].attrs)

src/xclim/indices/fire/_cffwis.py

@@ -848,14 +848,13 @@ def _fire_weather_calc(  # noqa: C901  # pylint: disable=R0912, R0915
                 ind_prevs["DMC"],
             )
         if "FFMC" in outputs:
-            with np.errstate(divide="ignore", invalid="ignore"):
-                out["FFMC"][..., it] = _fine_fuel_moisture_code(
-                    tas[..., it],
-                    pr[..., it],
-                    ws[..., it],
-                    rh[..., it],
-                    ind_prevs["FFMC"],
-                )
+            out["FFMC"][..., it] = _fine_fuel_moisture_code(
+                tas[..., it],
+                pr[..., it],
+                ws[..., it],
+                rh[..., it],
+                ind_prevs["FFMC"],
+            )
         if "ISI" in outputs:
             out["ISI"][..., it] = initial_spread_index(ws[..., it], out["FFMC"][..., it])
         if "BUI" in outputs:

src/xclim/indices/stats.py

@@ -51,10 +51,8 @@ def _fitfunc_1d(arr, *, dist, nparams, method, **fitkwargs):
 
     # Estimate parameters
     if method in ["ML", "MLE"]:
-        with np.errstate(invalid="ignore"):
-            with np.errstate(divide="ignore"):
-                args, kwargs = _fit_start(x, dist.name, **fitkwargs)
-            params = dist.fit(x, *args, method="mle", **kwargs, **fitkwargs)
+        args, kwargs = _fit_start(x, dist.name, **fitkwargs)
+        params = dist.fit(x, *args, method="mle", **kwargs, **fitkwargs)
     elif method == "MM":
         params = dist.fit(x, method="mm", **fitkwargs)
     elif method in ["MSE", "MPS"]:
@@ -63,8 +61,8 @@ def _fitfunc_1d(arr, *, dist, nparams, method, **fitkwargs):
         for i, arg in enumerate(args):
             guess[param_info[i]] = arg
 
-        fitresult = scipy.stats.fit(dist=dist, data=x, method="mse", guess=guess, **fitkwargs)
-        params = fitresult.params
+        fit_result = scipy.stats.fit(dist=dist, data=x, method="mse", guess=guess, **fitkwargs)
+        params = fit_result.params
     elif method == "PWM":
         # lmoments3 will raise an error if only dist.numargs + 2 values are provided
         if len(x) <= dist.numargs + 2:
@@ -618,11 +616,8 @@ def _fit_start(x, dist: str, **fitkwargs: Any) -> tuple[tuple, dict]:
         x_pos = x_pos[x_pos > 0]
         # MLE estimation
         log_x_pos = np.log(x_pos)
-        # ignore invalid values occurring in the log calculations
-        with np.errstate(invalid="ignore"), warnings.catch_warnings():
-            shape0 = log_x_pos.std()
-            warnings.filterwarnings("ignore", message="Mean of empty slice.", category=RuntimeWarning)
-            scale0 = np.exp(log_x_pos.mean())
+        shape0 = log_x_pos.std()
+        scale0 = np.exp(log_x_pos.mean())
         kwargs = {"scale": scale0, "loc": loc0}
         return (shape0,), kwargs
 

tests/test_analog.py

@@ -70,7 +70,13 @@ def test_spatial_analogs(method, open_dataset):
     target = data.sel(lat=46.1875, lon=-72.1875, time=slice("1970", "1990"))
     candidates = data.sel(time=slice("1970", "1990"))
 
-    out = xca.spatial_analogs(target, candidates, method=method)
+    # Ensure division by 0 operation is announced for kldiv
+    if method == "kldiv":
+        with pytest.warns(RuntimeWarning):
+            out = xca.spatial_analogs(target, candidates, method=method)
+    else:
+        out = xca.spatial_analogs(target, candidates, method=method)
+
     # Special case since scikit-learn updated to 1.2.0 (and again at 1.3)
     if method == "friedman_rafsky":
         diss[method][42, 105] = np.nan

tests/test_flags.py

@@ -38,7 +38,7 @@ def test_tas_temperature_flags(self, vars_dropped, flags, tas_series, tasmax_ser
 
     def test_pr_precipitation_flags(self, pr_series):
         # Pint <=0.24.4 has precision errors : (1 / 3600 / 24 kg m-2 s-1  = 0.999999998 mm/d )
-        pytest.importorskip("pint", minversion="0.25")
+        pytest.importorskip("pint", minversion="0.25", reason="Precipitation flag calculations require newer `pint`")
         pr = pr_series(np.zeros(365), start="1971-01-01")
         pr += 1 / 3600 / 24
         pr[0:7] += 10 / 3600 / 24
@@ -58,7 +58,7 @@ def test_pr_precipitation_flags(self, pr_series):
 
     def test_suspicious_pr_data(self, pr_series):
         # Pint <=0.24.4 has precision errors : (1 / 3600 / 24 kg m-2 s-1  = 0.999999998 mm/d )
-        pytest.importorskip("pint", minversion="0.25")
+        pytest.importorskip("pint", minversion="0.25", reason="Precipitation flag calculations require newer `pint`")
         bad_pr = pr_series(np.zeros(365), start="1971-01-01")
 
         # Add some strangeness

tests/test_indices.py

@@ -14,7 +14,6 @@
 from __future__ import annotations
 
 import calendar
-import warnings
 
 import numpy as np
 import pandas as pd
@@ -474,7 +473,7 @@ def test_huglin_index(self, method, end_date, freq, values, cap_value, open_data
         else:
             if method == "icclim":
                 # The 'icclim' method is an alias for 'huglin'
-                with pytest.warns(DeprecationWarning):
+                with pytest.warns(DeprecationWarning) as record:
                     # find lat implicitly
                     hi = xci.huglin_index(
                         tasmax=tasmax,
@@ -484,6 +483,7 @@ def test_huglin_index(self, method, end_date, freq, values, cap_value, open_data
                         freq=freq,
                         cap_value=cap_value,
                     )
+                    assert "Method 'icclim' is deprecated. Use 'stepwise' instead" in record[0].message.args[0]
             else:
                 # find lat implicitly
                 hi = xci.huglin_index(
@@ -744,7 +744,7 @@ def test_standardized_precipitation_index(
         self, freq, window, dist, method, values, diff_tol, open_dataset, no_numbagg
     ):
         if method == "ML" and freq == "D" and Version(__numpy_version__) < Version("2.0.0"):
-            pytest.skip("Skipping SPI/ML/D on older numpy")
+            pytest.skip("Skipping SPI/ML/D for numpy below v2.0")
 
         # change `dist` to a lmoments3 object if needed
         if method == "PWM":
@@ -882,7 +882,7 @@ def test_standardized_precipitation_evapotranspiration_index(
         self, freq, window, dist, method, values, diff_tol, open_dataset
     ):
         if method == "ML" and freq == "D" and Version(__numpy_version__) < Version("2.0.0"):
-            pytest.skip("Skipping SPI/ML/D on older numpy")
+            pytest.skip("Skipping SPI/ML/D for numpy below v2.0")
 
         ds = open_dataset("sdba/CanESM2_1950-2100.nc").isel(location=1).sel(time=slice("1950", "2000"))
         pr = ds.pr
@@ -1168,7 +1168,7 @@ def test_standardized_streamflow_index(
     )
     def test_standardized_groundwater_index(self, freq, window, dist, method, values, diff_tol, open_dataset):
         if method == "ML" and freq == "D" and Version(__numpy_version__) < Version("2.0.0"):
-            pytest.skip("Skipping SPI/ML/D on older numpy")
+            pytest.skip("Skipping SPI/ML/D for numpy below v2.0")
         ds = open_dataset("Raven/gwl_obs.nc")
         gwl0 = ds.gwl
 
@@ -1178,8 +1178,20 @@ def test_standardized_groundwater_index(self, freq, window, dist, method, values
         fitkwargs = {}
         if method == "APP":
             fitkwargs["floc"] = 0
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", message="Degrees of freedom <= 0 for slice")
+
+        if freq == "MS" and dist == "lognorm" and method == "ML":
+            with pytest.warns(RuntimeWarning) as record:
+                params = standardized_index_fit_params(
+                    gwl_cal,
+                    freq=freq,
+                    window=window,
+                    dist=dist,
+                    method=method,
+                    fitkwargs=fitkwargs,
+                    zero_inflated=True,
+                )
+                assert "Degrees of freedom <= 0 for slice" in record[0].message.args[0]
+        else:
             params = standardized_index_fit_params(
                 gwl_cal,
                 freq=freq,

tests/test_precip.py

@@ -492,7 +492,8 @@ def get_snowfall(cls, nimbus):
             (
                 xr.open_dataset(nimbus.fetch(cls.pr_file), engine="h5netcdf"),
                 xr.open_dataset(nimbus.fetch(cls.tasmin_file), engine="h5netcdf"),
-            )
+            ),
+            compat="no_conflicts",
         )
         return convert.snowfall_approximation(dnr.pr, tas=dnr.tasmin, thresh="-0.5 degC", method="binary")
 
@@ -702,7 +703,8 @@ def get_snowfall(cls, nimbus):
             (
                 xr.open_dataset(nimbus.fetch(cls.pr_file), engine="h5netcdf"),
                 xr.open_dataset(nimbus.fetch(cls.tasmin_file), engine="h5netcdf"),
-            )
+            ),
+            compat="no_conflicts",
         )
         return convert.snowfall_approximation(dnr.pr, tas=dnr.tasmin, thresh="-0.5 degC", method="binary")
 

tests/test_stats.py

@@ -285,13 +285,20 @@ def test_not_enough_unique_values(
     ):
         lmom = pytest.importorskip("lmoments3.distr")
         lm3dc = getattr(lmom, lm3_dist_map[dist])
-        time = xr.date_range("2000-01-01", "2000-12-31", freq="M")
-        # Some distributions have no parameter but we want to ensure we have at least one
+        time = xr.date_range("2000-01-01", "2000-12-31", freq="ME")
+        # Some distributions have no parameter, but we want to ensure we have at least one
         # unique value
         unique_values = np.arange(lm3dc.numargs or 1)
 
         da = xr.DataArray(np.random.choice(unique_values, time.size), coords=dict(time=("time", time)))
-        out = stats.fit(da, dist=lm3dc, method="PWM").compute()
+        with pytest.warns() as record:
+            out = stats.fit(da, dist=lm3dc, method="PWM").compute()
+
+            if dist in ["expon", "gumbel_r", "norm"]:
+                assert record[0].category is UserWarning
+            elif dist in ["gamma", "genextreme", "genpareto", "pearson3", "weibull_min"]:
+                assert record[0].category is RuntimeWarning
+
         assert out.isnull().all()
 
 

tox.toml

@@ -19,7 +19,7 @@ deps = [
   "lmoments3 >=1.0.7",
   "h5netcdf >=1.5.0",
   "netcdf4",
-  "pytest >=8.0.0",
+  "pytest >=9.0.0",
   "xsdba >=0.4.0"
 ]
 commands = [
@@ -28,9 +28,6 @@ commands = [
 
 [env.doctests]
 labels = ["doctests"]
-deps = [
-  "pytest >=9.0"
-]
 description = "Run doctests with pytest under {basepython}"
 commands = [
   ["python", "-c", "from xclim.testing.utils import run_doctests; run_doctests()"]