[pre-commit.ci] auto fixes from pre-commit.com hooks

icclim/ecad_functions.py

@@ -3,7 +3,7 @@
 metadata to it.
 """
 import re
-from typing import Callable, Optional, Tuple, Any
+from typing import Any, Callable, Optional, Tuple
 from warnings import warn
 
 import numpy as np
@@ -823,7 +823,11 @@ def _compute_percentile_doy(
     callback: Callable = None,
 ) -> DataArray:
     per = percentile_doy(
-        da, window, percentile, alpha=interpolation.alpha, beta=interpolation.beta,
+        da,
+        window,
+        percentile,
+        alpha=interpolation.alpha,
+        beta=interpolation.beta,
     )
     if callback is not None:
         callback(50)
@@ -859,7 +863,10 @@ def _filter_in_wet_days(da: DataArray, dry_day_value: float):
 
 
 def _compute_threshold_index(
-    da: DataArray, threshold: float, freq_config: dict, xclim_index_fun: Callable,
+    da: DataArray,
+    threshold: float,
+    freq_config: dict,
+    xclim_index_fun: Callable,
 ) -> DataArray:
     result = xclim_index_fun(da, thresh=f"{threshold} °C", **freq_config)
     return result
@@ -877,7 +884,11 @@ def _compute_spell_duration(
     xclim_index_fun: Callable,
 ) -> Tuple[DataArray, Optional[DataArray]]:
     per = _compute_percentile_doy(
-        cf_var.reference_da, per_thresh, per_window, per_interpolation, callback,
+        cf_var.reference_da,
+        per_thresh,
+        per_window,
+        per_interpolation,
+        callback,
     )
     run_bootstrap = _can_run_bootstrap(cf_var)
     result = xclim_index_fun(
@@ -950,16 +961,26 @@ def compute_compound_index(
         Otherwise, returns the index_result
     """
     tas_per = _compute_percentile_doy(
-        tas.reference_da, tas_per_thresh, per_window, per_interpolation, callback,
+        tas.reference_da,
+        tas_per_thresh,
+        per_window,
+        per_interpolation,
+        callback,
     )
     tas_per = tas_per.squeeze(PERCENTILES_COORD, drop=True)
     pr_in_base = _filter_in_wet_days(pr.reference_da, dry_day_value=np.NAN)
     pr_out_of_base = _filter_in_wet_days(pr.study_da, dry_day_value=0)
     pr_per = _compute_percentile_doy(
-        pr_in_base, pr_per_thresh, per_window, per_interpolation, callback,
+        pr_in_base,
+        pr_per_thresh,
+        per_window,
+        per_interpolation,
+        callback,
     )
     pr_per = pr_per.squeeze(PERCENTILES_COORD, drop=True)
-    result = xclim_index_fun(tas.study_da, tas_per, pr_out_of_base, pr_per, **freq_config)
+    result = xclim_index_fun(
+        tas.study_da, tas_per, pr_out_of_base, pr_per, **freq_config
+    )
     if save_percentile:
         # FIXME, not consistent with other percentile based indices
         #        We should probably return a Tuple (res, [tas_per, pr_per])
@@ -982,7 +1003,11 @@ def _compute_rxxptot(
         base_wet_days, per_interpolation, pr_per_thresh
     )
     result = atmos.fraction_over_precip_thresh(
-        pr.study_da, per, thresh="1 mm/day", **freq_config, bootstrap=False,
+        pr.study_da,
+        per,
+        thresh="1 mm/day",
+        **freq_config,
+        bootstrap=False,
     ).squeeze(PERCENTILES_COORD, drop=True)
     result = result * 100
     result.attrs["units"] = "%"
@@ -1004,7 +1029,11 @@ def _compute_rxxp(
         base_wet_days, per_interpolation, pr_per_thresh
     )
     result = atmos.days_over_precip_thresh(
-        pr.study_da, per, thresh="1 mm/day", **freq_config, bootstrap=False,
+        pr.study_da,
+        per,
+        thresh="1 mm/day",
+        **freq_config,
+        bootstrap=False,
     )
     result = result.squeeze(PERCENTILES_COORD, drop=True)
     if is_percent:
@@ -1027,10 +1056,17 @@ def _compute_temperature_percentile_index(
 ) -> Tuple[DataArray, Optional[DataArray]]:
     run_bootstrap = _can_run_bootstrap(cf_var)
     per = _compute_percentile_doy(
-        cf_var.reference_da, tas_per_thresh, per_window, per_interpolation, callback,
+        cf_var.reference_da,
+        tas_per_thresh,
+        per_window,
+        per_interpolation,
+        callback,
     ).compute()
     result = xclim_index_fun(
-        cf_var.study_da, per, **freq_config, bootstrap=run_bootstrap,
+        cf_var.study_da,
+        per,
+        **freq_config,
+        bootstrap=run_bootstrap,
     ).squeeze(PERCENTILES_COORD, drop=True)
     if run_bootstrap:
         result = _add_bootstrap_meta(result, per)

icclim/main.py

@@ -113,13 +113,14 @@ def index(
     index_name: str | None = None,  # optional when computing user_indices
     var_name: str | list[str] | None = None,
     slice_mode: SliceMode = Frequency.YEAR,
-    time_range: list[datetime] = None, # TODO: use dateparser to accept strings
+    time_range: list[datetime] = None,  # TODO: use dateparser to accept strings
     out_file: str | None = None,
     threshold: float | list[float] | None = None,
     callback: Callable[[int], None] = log.callback,
     callback_percentage_start_value: int = 0,
     callback_percentage_total: int = 100,
-    base_period_time_range: list[datetime] | None = None,  # TODO: use dateparser to accept strings
+    base_period_time_range: list[datetime]
+    | None = None,  # TODO: use dateparser to accept strings
     window_width: int = 5,
     only_leap_years: bool = False,
     ignore_Feb29th: bool = False,

icclim/models/frequency.py

@@ -7,10 +7,9 @@
 
 from datetime import datetime, timedelta
 from enum import Enum
-from typing import Callable, List, Tuple, Union, Dict
+from typing import Callable, Dict, List, Tuple, Union
 
 import cftime
-
 import dateparser
 import numpy as np
 import pandas as pd
@@ -75,7 +74,7 @@ def add_time_bounds(da: DataArray) -> tuple[DataArray, DataArray]:
                         end_month + 1,
                         1,
                         calendar=first_time.calendar,
-                    )- timedelta(days=1)
+                    ) - timedelta(days=1)
                 else:
                     end = cftime.datetime(
                         year_of_season_end,
@@ -86,7 +85,9 @@ def add_time_bounds(da: DataArray) -> tuple[DataArray, DataArray]:
             else:
                 start = pd.to_datetime(f"{year}-{start_month}-{start_day}")
                 if end_day is None:
-                    end = pd.to_datetime(f"{year_of_season_end}-{end_month + 1}") - timedelta(days=1)
+                    end = pd.to_datetime(
+                        f"{year_of_season_end}-{end_month + 1}"
+                    ) - timedelta(days=1)
                 else:
                     end = pd.to_datetime(f"{year_of_season_end}-{end_month}-{end_day}")
             new_time_axis.append(start + (end - start) / 2)
@@ -249,7 +250,6 @@ class Frequency(Enum):
        Do not use as is, use `slice_mode` with "month", "season" keywords instead.
     """
 
-
     def __init__(self, freq: _Freq):
         self._freq = freq
 
@@ -346,7 +346,7 @@ def _get_frequency_from_list(slice_mode_list: list) -> Frequency:
     return custom_freq
 
 
-def _build_frequency_filtered_by_month(months: List[int]):
+def _build_frequency_filtered_by_month(months: list[int]):
     return _Freq(
         indexer=dict(month=months),
         post_processing=_get_time_bounds_updater("MS"),

icclim/models/user_index_config.py

@@ -4,8 +4,8 @@
 from enum import Enum
 from typing import Any, Callable, Literal
 
-from xclim.core.calendar import select_time
 from xarray.core.dataarray import DataArray
+from xclim.core.calendar import select_time
 
 from icclim.icclim_exceptions import InvalidIcclimArgumentError
 from icclim.models.frequency import Frequency

icclim/tests/test_frequency.py

@@ -65,12 +65,13 @@ def test_winter(self):
         assert freq.post_processing is not None
 
     def test_lookup_season_between_dates(self):
-        freq = Frequency.lookup(["season",["07-19","08-14"]])
+        freq = Frequency.lookup(["season", ["07-19", "08-14"]])
         assert freq == Frequency.CUSTOM
         assert freq.pandas_freq == "AS-JUL"
         assert freq.accepted_values == []
         assert freq.post_processing is not None
 
+
 class Test_seasons_resampler:
     def test_simple(self):
         # WHEN
@@ -104,16 +105,17 @@ def test_winter(self):
     @pytest.mark.parametrize("use_cf", [True, False])
     def test_between_dates(self, use_cf):
         # WHEN
-        test_da = filter_months(stub_tas(use_cftime=use_cf), [11, 12, 1])\
-            .resample(time="AS-NOV")\
+        test_da = (
+            filter_months(stub_tas(use_cftime=use_cf), [11, 12, 1])
+            .resample(time="AS-NOV")
             .mean()
+        )
         da_res, time_bds_res = get_seasonal_time_updater(11, 1, 2, 30)(test_da)
         # THEN
-        np.testing.assert_array_equal(1, da_res) # data must be unchanged
+        np.testing.assert_array_equal(1, da_res)  # data must be unchanged
         assert time_bds_res[0].data[0] == pd.to_datetime("2041-11-02")
         assert time_bds_res[0].data[1] == pd.to_datetime("2042-01-30")
 
 
-
 def filter_months(da, month_list: list[int]):
     return da.sel(time=da.time.dt.month.isin(month_list))

icclim/tests/test_generated_api.py

@@ -104,7 +104,7 @@ def test_txx__season_slice_mode():
     # THEN
     # missing values for nov, dec of first period
     np.testing.assert_array_equal(res.TXx.isel(time=0), np.NAN)
-    np.testing.assert_array_equal(res.TXx.isel(time=1), 30.)
+    np.testing.assert_array_equal(res.TXx.isel(time=1), 30.0)
     np.testing.assert_array_equal(
         res.time_bounds.isel(time=0),
         [np.datetime64("2041-11-01"), np.datetime64("2042-02-28")],
@@ -137,7 +137,9 @@ def test_txx__months_slice_mode():
         (CalcOperation.MAX_NUMBER_OF_CONSECUTIVE_EVENTS, 1, 1),
     ],
 )
-def test_custom_index__season_slice_mode(operator, expectation_year_1, expectation_year_2):
+def test_custom_index__season_slice_mode(
+    operator, expectation_year_1, expectation_year_2
+):
     tas = stub_tas(2.0)
     tas.loc[{"time": "2042-01-01"}] = 303.15
     tas.loc[{"time": "2042-12-01"}] = 280.15
@@ -164,7 +166,9 @@ def test_custom_index__season_slice_mode(operator, expectation_year_1, expectati
         (CalcOperation.RUN_SUM, 14, 14),
     ],
 )
-def test_custom_index_run_algos__season_slice_mode(operator, expectation_year_1, expectation_year_2):
+def test_custom_index_run_algos__season_slice_mode(
+    operator, expectation_year_1, expectation_year_2
+):
     tas = stub_tas(2.0)
     res = icclim.custom_index(
         in_files=tas,

icclim/tests/test_main.py

@@ -176,7 +176,7 @@ def test_indices_all_ignore_error(self):
             in_files=ds,
             out_file=self.OUTPUT_FILE,
             ignore_error=True,
-            slice_mode="DJF"
+            slice_mode="DJF",
         ).compute()
         for i in EcadIndex:
             # No variable in input to compute snow indices

icclim/tests/test_utils.py

@@ -3,14 +3,13 @@
 import numpy as np
 import pandas as pd
 import xarray
+import xarray as xr
 
 from icclim.models.frequency import Frequency
 from icclim.models.index_config import CfVariable
 from icclim.models.user_index_config import UserIndexConfig
 
-import xarray as xr
-
-VALUE_COUNT = 365 * 5 + 1 # 5 years of data (with 1 leap year)
+VALUE_COUNT = 365 * 5 + 1  # 5 years of data (with 1 leap year)
 COORDS = dict(
     lat=[42],
     lon=[42],
@@ -20,6 +19,7 @@
 
 CF_TIME_RANGE = xr.cftime_range("2042-01-01", periods=VALUE_COUNT, freq="D")
 
+
 def stub_user_index(cf_vars: List[CfVariable]):
     return UserIndexConfig(
         index_name="Yolo", calc_operation="noop", freq=Frequency.MONTH, cf_vars=cf_vars