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

icclim/ecad/ecad_functions.py

@@ -27,7 +27,7 @@
 def gd4(config: IndexConfig) -> DataArray:
     return _compute_threshold_index(
         da=config.tas.study_da,
-        threshold=4.0 ,
+        threshold=4.0,
         freq=config.frequency,
         xclim_index_fun=atmos.growing_degree_days,
     )
@@ -36,7 +36,7 @@ def gd4(config: IndexConfig) -> DataArray:
 def cfd(config: IndexConfig) -> DataArray:
     return _compute_threshold_index(
         da=config.tasmin.study_da,
-        threshold=0.0 ,
+        threshold=0.0,
         freq=config.frequency,
         xclim_index_fun=atmos.consecutive_frost_days,
     )
@@ -45,7 +45,7 @@ def cfd(config: IndexConfig) -> DataArray:
 def fd(config: IndexConfig) -> DataArray:
     return _compute_threshold_index(
         da=config.tasmin.study_da,
-        threshold=0.0 ,
+        threshold=0.0,
         freq=config.frequency,
         xclim_index_fun=atmos.frost_days,
     )
@@ -63,7 +63,7 @@ def hd17(config: IndexConfig) -> DataArray:
 def id(config: IndexConfig) -> DataArray:
     return _compute_threshold_index(
         da=config.tasmax.study_da,
-        threshold=0.0 ,
+        threshold=0.0,
         freq=config.frequency,
         xclim_index_fun=atmos.ice_days,
     )
@@ -320,7 +320,7 @@ def r75p(config: IndexConfig) -> tuple[DataArray, DataArray | None]:
     return _compute_rxxp(
         pr=config.pr,
         freq=config.frequency,
-        pr_per_thresh=75.,
+        pr_per_thresh=75.0,
         per_interpolation=config.interpolation,
         save_percentile=config.save_percentile,
         is_percent=config.is_percent,
@@ -331,7 +331,7 @@ def r75ptot(config: IndexConfig) -> tuple[DataArray, DataArray | None]:
     return _compute_rxxptot(
         pr=config.pr,
         freq=config.frequency,
-        pr_per_thresh=75.,
+        pr_per_thresh=75.0,
         per_interpolation=config.interpolation,
         save_percentile=config.save_percentile,
     )
@@ -341,7 +341,7 @@ def r95p(config: IndexConfig) -> tuple[DataArray, DataArray | None]:
     return _compute_rxxp(
         pr=config.pr,
         freq=config.frequency,
-        pr_per_thresh=95.,
+        pr_per_thresh=95.0,
         per_interpolation=config.interpolation,
         save_percentile=config.save_percentile,
         is_percent=config.is_percent,
@@ -352,7 +352,7 @@ def r95ptot(config: IndexConfig) -> tuple[DataArray, DataArray | None]:
     return _compute_rxxptot(
         pr=config.pr,
         freq=config.frequency,
-        pr_per_thresh=95.,
+        pr_per_thresh=95.0,
         per_interpolation=config.interpolation,
         save_percentile=config.save_percentile,
     )

icclim/generic_indices/generic_index_functions.py

@@ -23,22 +23,23 @@
 from icclim.models.index_config import IndexConfig
 from icclim.models.quantile_interpolation import QuantileInterpolation
 
+
 def _can_run_bootstrap(cf_var: CfVariable) -> bool:
     """Avoid bootstrapping if there is one single year overlapping
     or no year overlapping or all year overlapping.
     """
     study_years = np.unique(cf_var.study_da.indexes.get("time").year)
     overlapping_years = np.unique(
         cf_var.study_da.sel(time=_get_ref_period_slice(cf_var.reference_da))
-            .indexes.get("time")
-            .year
+        .indexes.get("time")
+        .year
     )
     return 1 < len(overlapping_years) < len(study_years)
 
 
 def _get_ref_period_slice(da: DataArray) -> slice:
     time_length = len(da.time)
-    return slice(*da.time[0:: time_length - 1].dt.strftime("%Y-%m-%d").values)
+    return slice(*da.time[0 :: time_length - 1].dt.strftime("%Y-%m-%d").values)
 
 
 def _to_percent(da: DataArray, sampling_freq: Frequency) -> DataArray:
@@ -93,11 +94,11 @@ def _add_bootstrap_meta(result: DataArray, per: DataArray) -> DataArray:
 
 
 def _compute_percentile_doy(
-        cf_var: CfVariable,
-        percentile: float,
-        window: int = 5,
-        interpolation=QuantileInterpolation.MEDIAN_UNBIASED,
-        callback: Callable = None,
+    cf_var: CfVariable,
+    percentile: float,
+    window: int = 5,
+    interpolation=QuantileInterpolation.MEDIAN_UNBIASED,
+    callback: Callable = None,
 ) -> (DataArray, bool):
     if PercentileDataArray.is_compatible(cf_var.reference_da):
         per = cf_var.reference_da
@@ -117,7 +118,7 @@ def _compute_percentile_doy(
 
 
 def _compute_precip_percentile_over_period(
-        cf_var: CfVariable, interpolation: QuantileInterpolation, percentiles: float
+    cf_var: CfVariable, interpolation: QuantileInterpolation, percentiles: float
 ) -> DataArray:
     if PercentileDataArray.is_compatible(cf_var.reference_da):
         return cf_var.reference_da
@@ -146,26 +147,26 @@ def _filter_in_wet_days(da: DataArray, dry_day_value: float):
 
 
 def _compute_threshold_index(
-        da: DataArray,
-        threshold: float,
-        freq: Frequency,
-        xclim_index_fun: Callable,
+    da: DataArray,
+    threshold: float,
+    freq: Frequency,
+    xclim_index_fun: Callable,
 ) -> DataArray:
     return xclim_index_fun(
         da, thresh=f"{threshold} °C", **freq.build_frequency_kwargs()
     )
 
 
 def _compute_spell_duration(
-        cf_var: CfVariable,
-        freq: Frequency,
-        per_window: int,
-        per_thresh: float,
-        per_interpolation: QuantileInterpolation,
-        min_spell_duration: int,
-        save_percentile: bool,
-        callback: Callable,
-        xclim_index_fun: Callable,
+    cf_var: CfVariable,
+    freq: Frequency,
+    per_window: int,
+    per_thresh: float,
+    per_interpolation: QuantileInterpolation,
+    min_spell_duration: int,
+    save_percentile: bool,
+    callback: Callable,
+    xclim_index_fun: Callable,
 ) -> tuple[DataArray, DataArray | None]:
     per, run_bootstrap = _compute_percentile_doy(
         cf_var,
@@ -195,16 +196,16 @@ def _compute_spell_duration(
 
 
 def compute_compound_index(
-        tas: CfVariable,
-        pr: CfVariable,
-        freq: Frequency,
-        tas_per_thresh: int,
-        pr_per_thresh: int,
-        per_window: int,
-        per_interpolation: QuantileInterpolation,
-        save_percentile: bool,
-        callback: Callable,
-        xclim_index_fun: Callable,
+    tas: CfVariable,
+    pr: CfVariable,
+    freq: Frequency,
+    tas_per_thresh: int,
+    pr_per_thresh: int,
+    per_window: int,
+    per_interpolation: QuantileInterpolation,
+    save_percentile: bool,
+    callback: Callable,
+    xclim_index_fun: Callable,
 ) -> DataArray:
     """CD, CW, WD, WW
 
@@ -260,11 +261,11 @@ def compute_compound_index(
 
 
 def _compute_rxxptot(
-        pr: CfVariable,
-        freq: Frequency,
-        pr_per_thresh: float,
-        per_interpolation: QuantileInterpolation,
-        save_percentile: bool,
+    pr: CfVariable,
+    freq: Frequency,
+    pr_per_thresh: float,
+    per_interpolation: QuantileInterpolation,
+    save_percentile: bool,
 ) -> tuple[DataArray, DataArray | None]:
     per = _compute_precip_percentile_over_period(pr, per_interpolation, pr_per_thresh)
     result = atmos.fraction_over_precip_thresh(
@@ -282,12 +283,12 @@ def _compute_rxxptot(
 
 
 def _compute_rxxp(
-        pr: CfVariable,
-        freq: Frequency,
-        pr_per_thresh: float,
-        per_interpolation: QuantileInterpolation,
-        save_percentile: bool,
-        is_percent: bool,
+    pr: CfVariable,
+    freq: Frequency,
+    pr_per_thresh: float,
+    per_interpolation: QuantileInterpolation,
+    save_percentile: bool,
+    is_percent: bool,
 ) -> tuple[DataArray, DataArray | None]:
     per = _compute_precip_percentile_over_period(pr, per_interpolation, pr_per_thresh)
     result = atmos.days_over_precip_thresh(
@@ -306,15 +307,15 @@ def _compute_rxxp(
 
 
 def _compute_temperature_percentile_index(
-        cf_var: CfVariable,
-        freq: Frequency,
-        tas_per_thresh: int,
-        per_window: int,
-        per_interpolation: QuantileInterpolation,
-        save_percentile: bool,
-        is_percent: bool,
-        callback: Callable,
-        xclim_index_fun: Callable,
+    cf_var: CfVariable,
+    freq: Frequency,
+    tas_per_thresh: int,
+    per_window: int,
+    per_interpolation: QuantileInterpolation,
+    save_percentile: bool,
+    is_percent: bool,
+    callback: Callable,
+    xclim_index_fun: Callable,
 ) -> tuple[DataArray, DataArray | None]:
     per, run_bootstrap = _compute_percentile_doy(
         cf_var,

icclim/generic_indices/generic_indices.py

@@ -76,10 +76,10 @@ def __init__(self, climate_index: ClimateIndex):
     )
 
 
-
-def tx_above_scalar(c)->DataArray:
+def tx_above_scalar(c) -> DataArray:
     pass
 
+
 # -- COUNT DAYS ABOVE
 # su
 days_above_scalar
@@ -128,5 +128,3 @@ def tx_above_scalar(c)->DataArray:
 #    - rxxpTOT
 #    - prcptot
 #    - handle units
-
-

icclim/main.py

@@ -18,8 +18,8 @@
 import xclim_catalog
 from xarray.core.dataarray import DataArray
 from xarray.core.dataset import Dataset
-
 from xclim_catalog import XclimIndices
+
 from icclim.ecad.ecad_functions import IndexConfig
 from icclim.ecad.ecad_indices import EcadIndex, get_season_excluded_indices
 from icclim.icclim_exceptions import InvalidIcclimArgumentError
@@ -149,7 +149,7 @@ def index(
     user_indice: UserIndexDict = None,
     transfer_limit_Mbytes: float = None,
     # additional kwargs
-    **kwargs: dict
+    **kwargs: dict,
 ) -> Dataset:
     """
     Main entry point for icclim to compute climate indices.

icclim/models/index_config.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from typing import Callable, Any
+from typing import Any, Callable
 
 from icclim.models.cf_variable import CfVariable
 from icclim.models.climate_index import ClimateIndex
-from icclim.models.constants import PR, TAS, TAS_MAX, TAS_MIN, SFC_WIND
+from icclim.models.constants import PR, SFC_WIND, TAS, TAS_MAX, TAS_MIN
 from icclim.models.frequency import Frequency
 from icclim.models.netcdf_version import NetcdfVersion
 from icclim.models.quantile_interpolation import QuantileInterpolation
@@ -55,17 +55,17 @@ class IndexConfig:
 
     def __init__(
         self,
-            frequency: Frequency,
-            netcdf_version: str | NetcdfVersion,
-            index: ClimateIndex | None,
-            cf_variables: list[CfVariable],
-            save_percentile: bool = False,
-            window_width: int | None = 5,
-            threshold: list[float] | float | None = None,
-            out_unit: str | None = None,
-            interpolation=QuantileInterpolation.MEDIAN_UNBIASED,
-            callback: Callable[[int], None] | None = None,
-            xclim_kwargs: dict[str, Any] | None = None,
+        frequency: Frequency,
+        netcdf_version: str | NetcdfVersion,
+        index: ClimateIndex | None,
+        cf_variables: list[CfVariable],
+        save_percentile: bool = False,
+        window_width: int | None = 5,
+        threshold: list[float] | float | None = None,
+        out_unit: str | None = None,
+        interpolation=QuantileInterpolation.MEDIAN_UNBIASED,
+        callback: Callable[[int], None] | None = None,
+        xclim_kwargs: dict[str, Any] | None = None,
     ):
         self.frequency = frequency
         self.cf_variables = cf_variables