[pre-commit.ci] pre-commit autoupdate (#1766)

<!--pre-commit.ci start-->
updates:
- [github.com/psf/black-pre-commit-mirror: 24.4.1 →
24.4.2](psf/black-pre-commit-mirror@24.4.1...24.4.2)
- [github.com/astral-sh/ruff-pre-commit: v0.3.7 →
v0.4.7](astral-sh/ruff-pre-commit@v0.3.7...v0.4.7)
- [github.com/pylint-dev/pylint: v3.1.0 →
v3.2.2](pylint-dev/pylint@v3.1.0...v3.2.2)
- [github.com/codespell-project/codespell: v2.2.6 →
v2.3.0](codespell-project/codespell@v2.2.6...v2.3.0)
- [github.com/gitleaks/gitleaks: v8.18.2 →
v8.18.3](gitleaks/gitleaks@v8.18.2...v8.18.3)
- [github.com/python-jsonschema/check-jsonschema: 0.28.2 →
0.28.4](python-jsonschema/check-jsonschema@0.28.2...0.28.4)
<!--pre-commit.ci end-->

.pre-commit-config.yaml

@@ -33,19 +33,19 @@ repos:
       - id: yamllint
         args: [ '--config-file=.yamllint.yaml' ]
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.4.1
+    rev: 24.4.2
     hooks:
       - id: black
   - repo: https://github.com/PyCQA/isort
     rev: 5.13.2
     hooks:
       - id: isort
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.7
+    rev: v0.4.7
     hooks:
       - id: ruff
   - repo: https://github.com/pylint-dev/pylint
-    rev: v3.1.0
+    rev: v3.2.2
     hooks:
       - id: pylint
         args: [ '--rcfile=.pylintrc.toml', '--errors-only', '--jobs=0', '--disable=import-error' ]
@@ -66,7 +66,7 @@ repos:
         args: [ '--py39-plus' ]
         additional_dependencies: [ 'pyupgrade==3.15.2' ]
       - id: nbqa-black
-        additional_dependencies: [ 'black==24.4.1' ]
+        additional_dependencies: [ 'black==24.4.2' ]
       - id: nbqa-isort
         additional_dependencies: [ 'isort==5.13.2' ]
   - repo: https://github.com/kynan/nbstripout
@@ -79,19 +79,19 @@ repos:
     rev: v0.3.9
     hooks:
       - id: blackdoc
-        additional_dependencies: [ 'black==24.4.1' ]
+        additional_dependencies: [ 'black==24.4.2' ]
         exclude: '(xclim/indices/__init__.py|docs/installation.rst)'
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.6
+    rev: v2.3.0
     hooks:
       - id: codespell
         additional_dependencies: [ 'tomli' ]
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.18.2
+    rev: v8.18.3
     hooks:
       - id: gitleaks
   - repo: https://github.com/python-jsonschema/check-jsonschema
-    rev: 0.28.2
+    rev: 0.28.4
     hooks:
       - id: check-github-workflows
       - id: check-readthedocs

CHANGES.rst

@@ -15,6 +15,8 @@ Internal changes
 * Synchronized tooling versions across ``pyproject.toml`` and ``tox.ini`` and pinned them to the latest stable releases in GitHub Workflows. (:pull:`1744`).
 * Fixed a few small spelling and grammar issues that were causing errors with `codespell`. Now ignoring `SVG` files. (:pull:`1769`).
 * Temporarily skipping the ``test_hawkins_sutton_smoke`` test due to strange behaviour with `xarray`. (:pull:`1769`).
+* Fixed some previously uncaught errors raised from recent versions of `pylint` and `codespell`. (:pull:`1772`).
+* Set the `doctest` examples to all use `h5netcdf` with worker-separated caches to load datasets. (:pull:`1772`).
 
 v0.49.0 (2024-05-02)
 --------------------

pyproject.toml

@@ -156,7 +156,7 @@ values = [
 
 [tool.codespell]
 skip = 'xclim/data/*.json,docs/_build,docs/notebooks/xclim_training/*.ipynb,docs/references.bib,__pycache__,*.gz,*.nc,*.png,*.svg,*.whl'
-ignore-words-list = "absolue,bloc,bui,callendar,degreee,environnement,hanel,inferrable,lond,nam,nd,ressources,sie,vas"
+ignore-words-list = "absolue,bloc,bui,callendar,degreee,environnement,hanel,inferrable,lond,nam,nd,ressources,socio-economic,sie,vas"
 
 [tool.coverage.run]
 relative_files = true

tests/test_calendar.py

@@ -90,6 +90,8 @@ def test_time_bnds_irregular(typ):
     elif typ == "pd":
         start = pd.date_range("1990-01-01", periods=24, freq="MS")
         end = pd.date_range("1990-01-01 23:59:59.999999999", periods=24, freq="ME")
+    else:
+        raise ValueError("`typ` must be 'pd' or 'xr'")
 
     time = start + (end - start) / 2
 

tests/test_indices.py

@@ -2971,6 +2971,8 @@ def test_rain_season(pr_series, result_type, method_dry_start):
     elif result_type == "end_cond_fails":
         pr[{"time": 99 + 20 - 1}] = 5
         out_exp = [3, np.NaN, 363]
+    else:
+        raise ValueError(f"Unknown result_type: {result_type}")
 
     out = {}
     out["start"], out["end"], out["length"] = xci.rain_season(

xclim/indices/_agro.py

@@ -506,7 +506,7 @@ def cool_night_index(
     Examples
     --------
     >>> from xclim.indices import cool_night_index
-    >>> tasmin = xr.open_dataset(path_to_tasmin_file).tasmin
+    >>> tasmin = open_dataset(path_to_tasmin_file).tasmin
     >>> cni = cool_night_index(tasmin)
 
     References
@@ -1170,7 +1170,7 @@ def standardized_precipitation_index(
     -------
     >>> from datetime import datetime
     >>> from xclim.indices import standardized_precipitation_index
-    >>> ds = xr.open_dataset(path_to_pr_file)
+    >>> ds = open_dataset(path_to_pr_file)
     >>> pr = ds.pr
     >>> cal_start, cal_end = "1990-05-01", "1990-08-31"
     >>> spi_3 = standardized_precipitation_index(

xclim/indices/_anuclim.py

@@ -128,7 +128,7 @@ def temperature_seasonality(
     The following would compute for each grid cell of file `tas.day.nc` the annual temperature seasonality:
 
     >>> import xclim.indices as xci
-    >>> t = xr.open_dataset(path_to_tas_file).tas
+    >>> t = open_dataset(path_to_tas_file).tas
     >>> tday_seasonality = xci.temperature_seasonality(t)
     >>> t_weekly = xci.tg_mean(t, freq="7D")
     >>> tweek_seasonality = xci.temperature_seasonality(t_weekly)
@@ -179,7 +179,7 @@ def precip_seasonality(pr: xarray.DataArray, freq: str = "YS") -> xarray.DataArr
     The following would compute for each grid cell of file `pr.day.nc` the annual precipitation seasonality:
 
     >>> import xclim.indices as xci
-    >>> p = xr.open_dataset(path_to_pr_file).pr
+    >>> p = open_dataset(path_to_pr_file).pr
     >>> pday_seasonality = xci.precip_seasonality(p)
     >>> p_weekly = xci.precip_accumulation(p, freq="7D")
 
@@ -242,7 +242,7 @@ def tg_mean_warmcold_quarter(
     warmest quarter mean temperature:
 
     >>> from xclim.indices import tg_mean_warmcold_quarter
-    >>> t = xr.open_dataset(path_to_tas_file)
+    >>> t = open_dataset(path_to_tas_file)
     >>> t_warm_qrt = tg_mean_warmcold_quarter(tas=t.tas, op="warmest")
 
     Notes
@@ -353,7 +353,7 @@ def prcptot_wetdry_quarter(
     The following would compute for each grid cell of file `pr.day.nc` the annual wettest quarter total precipitation:
 
     >>> from xclim.indices import prcptot_wetdry_quarter
-    >>> p = xr.open_dataset(path_to_pr_file)
+    >>> p = open_dataset(path_to_pr_file)
     >>> pr_warm_qrt = prcptot_wetdry_quarter(pr=p.pr, op="wettest")
 
     Notes

xclim/indices/_multivariate.py

@@ -127,7 +127,7 @@ def cold_spell_duration_index(
     --------
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import cold_spell_duration_index
-    >>> tasmin = xr.open_dataset(path_to_tasmin_file).tasmin.isel(lat=0, lon=0)
+    >>> tasmin = open_dataset(path_to_tasmin_file).tasmin.isel(lat=0, lon=0)
     >>> tn10 = percentile_doy(tasmin, per=10).sel(percentiles=10)
     >>> cold_spell_duration_index(tasmin, tn10)
 
@@ -971,7 +971,7 @@ def precip_accumulation(
     precipitation at the seasonal frequency, ie DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import precip_accumulation
-    >>> pr_day = xr.open_dataset(path_to_pr_file).pr
+    >>> pr_day = open_dataset(path_to_pr_file).pr
     >>> prcp_tot_seasonal = precip_accumulation(pr_day, freq="QS-DEC")
     """
     if phase == "liquid":
@@ -1034,7 +1034,7 @@ def precip_average(
     precipitation at the seasonal frequency, ie DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import precip_average
-    >>> pr_day = xr.open_dataset(path_to_pr_file).pr
+    >>> pr_day = open_dataset(path_to_pr_file).pr
     >>> prcp_tot_seasonal = precip_average(pr_day, freq="QS-DEC")
     """
     if phase == "liquid":
@@ -1146,8 +1146,8 @@ def high_precip_low_temp(
     Example
     -------
     To compute the number of days with intense rainfall while minimum temperatures dip below -0.2C:
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
-    >>> tasmin = xr.open_dataset(path_to_tasmin_file).tasmin
+    >>> pr = open_dataset(path_to_pr_file).pr
+    >>> tasmin = open_dataset(path_to_tasmin_file).tasmin
     >>> high_precip_low_temp(
     ...     pr, tas=tasmin, pr_thresh="10 mm/d", tas_thresh="-0.2 degC"
     ... )
@@ -1204,7 +1204,7 @@ def days_over_precip_thresh(
     Examples
     --------
     >>> from xclim.indices import days_over_precip_thresh
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> p75 = pr.quantile(0.75, dim="time", keep_attrs=True)
     >>> r75p = days_over_precip_thresh(pr, p75)
     """
@@ -1333,7 +1333,7 @@ def tg90p(
     --------
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import tg90p
-    >>> tas = xr.open_dataset(path_to_tas_file).tas
+    >>> tas = open_dataset(path_to_tas_file).tas
     >>> tas_per = percentile_doy(tas, per=90).sel(percentiles=90)
     >>> hot_days = tg90p(tas, tas_per)
     """
@@ -1391,7 +1391,7 @@ def tg10p(
     --------
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import tg10p
-    >>> tas = xr.open_dataset(path_to_tas_file).tas
+    >>> tas = open_dataset(path_to_tas_file).tas
     >>> tas_per = percentile_doy(tas, per=10).sel(percentiles=10)
     >>> cold_days = tg10p(tas, tas_per)
     """
@@ -1449,7 +1449,7 @@ def tn90p(
     --------
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import tn90p
-    >>> tas = xr.open_dataset(path_to_tas_file).tas
+    >>> tas = open_dataset(path_to_tas_file).tas
     >>> tas_per = percentile_doy(tas, per=90).sel(percentiles=90)
     >>> hot_days = tn90p(tas, tas_per)
     """
@@ -1507,7 +1507,7 @@ def tn10p(
     --------
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import tn10p
-    >>> tas = xr.open_dataset(path_to_tas_file).tas
+    >>> tas = open_dataset(path_to_tas_file).tas
     >>> tas_per = percentile_doy(tas, per=10).sel(percentiles=10)
     >>> cold_days = tn10p(tas, tas_per)
     """
@@ -1565,7 +1565,7 @@ def tx90p(
     --------
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import tx90p
-    >>> tas = xr.open_dataset(path_to_tas_file).tas
+    >>> tas = open_dataset(path_to_tas_file).tas
     >>> tasmax_per = percentile_doy(tas, per=90).sel(percentiles=90)
     >>> hot_days = tx90p(tas, tasmax_per)
     """
@@ -1623,7 +1623,7 @@ def tx10p(
     --------
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import tx10p
-    >>> tas = xr.open_dataset(path_to_tas_file).tas
+    >>> tas = open_dataset(path_to_tas_file).tas
     >>> tasmax_per = percentile_doy(tas, per=10).sel(percentiles=10)
     >>> cold_days = tx10p(tas, tasmax_per)
     """
@@ -1762,7 +1762,7 @@ def warm_spell_duration_index(
     >>> from xclim.core.calendar import percentile_doy
     >>> from xclim.indices import warm_spell_duration_index
 
-    >>> tasmax = xr.open_dataset(path_to_tasmax_file).tasmax.isel(lat=0, lon=0)
+    >>> tasmax = open_dataset(path_to_tasmax_file).tasmax.isel(lat=0, lon=0)
     >>> tasmax_per = percentile_doy(tasmax, per=90).sel(percentiles=90)
     >>> warm_spell_duration_index(tasmax, tasmax_per)
     """

xclim/indices/_simple.py

@@ -103,7 +103,7 @@ def tg_mean(tas: xarray.DataArray, freq: str = "YS") -> xarray.DataArray:
     at the seasonal frequency, i.e. DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import tg_mean
-    >>> t = xr.open_dataset(path_to_tas_file).tas
+    >>> t = open_dataset(path_to_tas_file).tas
     >>> tg = tg_mean(t, freq="QS-DEC")
     """
     return select_resample_op(tas, op="mean", freq=freq)
@@ -428,7 +428,7 @@ def max_1day_precipitation_amount(
     The following would compute for each grid cell the highest 1-day total at an annual frequency:
 
     >>> from xclim.indices import max_1day_precipitation_amount
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> rx1day = max_1day_precipitation_amount(pr, freq="YS")
     """
     return select_resample_op(pr, op="max", freq=freq)
@@ -462,7 +462,7 @@ def max_n_day_precipitation_amount(
     The following would compute for each grid cell the highest 5-day total precipitation at an annual frequency:
 
     >>> from xclim.indices import max_n_day_precipitation_amount
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> out = max_n_day_precipitation_amount(pr, window=5, freq="YS")
     """
     # Rolling sum of the values
@@ -499,7 +499,7 @@ def max_pr_intensity(
     The following would compute the maximum 6-hour precipitation intensity at an annual frequency:
 
     >>> from xclim.indices import max_pr_intensity
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> out = max_pr_intensity(pr, window=5, freq="YS")
     """
     # Rolling sum of the values
@@ -569,7 +569,7 @@ def sfcWind_max(  # noqa: N802
     at the seasonal frequency, i.e. DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import sfcWind_max
-    >>> fg = xr.open_dataset(path_to_sfcWind_file).sfcWind
+    >>> fg = open_dataset(path_to_sfcWind_file).sfcWind
     >>> fg_max = sfcWind_max(fg, freq="QS-DEC")
     """
     return sfcWind.resample(time=freq).max(dim="time").assign_attrs(units=sfcWind.units)
@@ -610,7 +610,7 @@ def sfcWind_mean(  # noqa: N802
     at the seasonal frequency, i.e. DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import sfcWind_mean
-    >>> fg = xr.open_dataset(path_to_sfcWind_file).sfcWind
+    >>> fg = open_dataset(path_to_sfcWind_file).sfcWind
     >>> fg_mean = sfcWind_mean(fg, freq="QS-DEC")
     """
     return (
@@ -653,7 +653,7 @@ def sfcWind_min(  # noqa: N802
     at the seasonal frequency, i.e. DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import sfcWind_min
-    >>> fg = xr.open_dataset(path_to_sfcWind_file).sfcWind
+    >>> fg = open_dataset(path_to_sfcWind_file).sfcWind
     >>> fg_min = sfcWind_min(fg, freq="QS-DEC")
     """
     return sfcWind.resample(time=freq).min(dim="time").assign_attrs(units=sfcWind.units)

xclim/indices/_threshold.py

@@ -773,7 +773,7 @@ def daily_pr_intensity(
     precipitation >= 5 mm at seasonal frequency, i.e. DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import daily_pr_intensity
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> daily_int = daily_pr_intensity(pr, thresh="5 mm/day", freq="QS-DEC")
     """
     t = convert_units_to(thresh, pr, "hydro")
@@ -1142,7 +1142,7 @@ def growing_season_length(
     Examples
     --------
     >>> from xclim.indices import growing_season_length
-    >>> tas = xr.open_dataset(path_to_tas_file).tas
+    >>> tas = open_dataset(path_to_tas_file).tas
 
     For the Northern Hemisphere:
 
@@ -1228,7 +1228,7 @@ def frost_season_length(
     Examples
     --------
     >>> from xclim.indices import frost_season_length
-    >>> tasmin = xr.open_dataset(path_to_tasmin_file).tasmin
+    >>> tasmin = open_dataset(path_to_tasmin_file).tasmin
 
     For the Northern Hemisphere:
 
@@ -1411,7 +1411,7 @@ def frost_free_season_length(
     Examples
     --------
     >>> from xclim.indices import frost_season_length
-    >>> tasmin = xr.open_dataset(path_to_tasmin_file).tasmin
+    >>> tasmin = open_dataset(path_to_tasmin_file).tasmin
 
     For the Northern Hemisphere:
 
@@ -2604,7 +2604,7 @@ def wetdays(
     at the seasonal frequency, i.e. DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import wetdays
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> wd = wetdays(pr, thresh="5 mm/day", freq="QS-DEC")
     """
     thresh = convert_units_to(thresh, pr, "hydro")
@@ -2646,7 +2646,7 @@ def wetdays_prop(
     5 mm at the seasonal frequency, i.e. DJF, MAM, JJA, SON, DJF, etc.:
 
     >>> from xclim.indices import wetdays_prop
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> wd = wetdays_prop(pr, thresh="5 mm/day", freq="QS-DEC")
     """
     thresh = convert_units_to(thresh, pr, "hydro")
@@ -3179,7 +3179,7 @@ def dry_spell_frequency(
     Examples
     --------
     >>> from xclim.indices import dry_spell_frequency
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> dsf = dry_spell_frequency(pr=pr, op="sum")
     >>> dsf = dry_spell_frequency(pr=pr, op="max")
     """
@@ -3372,7 +3372,7 @@ def wet_spell_frequency(
     Examples
     --------
     >>> from xclim.indices import wet_spell_frequency
-    >>> pr = xr.open_dataset(path_to_pr_file).pr
+    >>> pr = open_dataset(path_to_pr_file).pr
     >>> dsf = wet_spell_frequency(pr=pr, op="sum")
     >>> dsf = wet_spell_frequency(pr=pr, op="max")
     """