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

for more information, see https://pre-commit.ci

climpred/bias_removal.py

@@ -338,9 +338,9 @@ def gaussian_bias_removal(
             hindcast_train._datasets["initialized"] = hindcast.get_initialized().sel(
                 init=train_time_init
             )
-            hindcast_test._datasets[
-                "initialized"
-            ] = hindcast.get_initialized().drop_sel(init=train_time_init)
+            hindcast_test._datasets["initialized"] = (
+                hindcast.get_initialized().drop_sel(init=train_time_init)
+            )
     else:
         assert train_test_split in ["unfair", "unfair-cv"]
         hindcast_train = hindcast

climpred/bootstrap.py

@@ -484,9 +484,9 @@ def resample_skill_loop(self, iterations, resample_dim, verify_kwargs):
             # resample uninitialized
             if not self.get_uninitialized():
                 # warn not found and therefore generate
-                self_for_loop._datasets[
-                    "uninitialized"
-                ] = self.generate_uninitialized().get_uninitialized()
+                self_for_loop._datasets["uninitialized"] = (
+                    self.generate_uninitialized().get_uninitialized()
+                )
             else:
                 self_for_loop._datasets["uninitialized"] = _resample(
                     self.get_uninitialized(), "member"

climpred/classes.py

@@ -1456,9 +1456,11 @@ def verify(
         reference = _check_valid_reference(reference)
         input_dict = {
             "ensemble": self._datasets["initialized"],
-            "control": self._datasets["control"]
-            if isinstance(self._datasets["control"], xr.Dataset)
-            else None,
+            "control": (
+                self._datasets["control"]
+                if isinstance(self._datasets["control"], xr.Dataset)
+                else None
+            ),
             "init": True,
         }
         result = self._apply_climpred_function(
@@ -1535,9 +1537,11 @@ def _compute_uninitialized(
         )
         input_dict = {
             "ensemble": self._datasets["uninitialized"],
-            "control": self._datasets["control"]
-            if isinstance(self._datasets["control"], xr.Dataset)
-            else None,
+            "control": (
+                self._datasets["control"]
+                if isinstance(self._datasets["control"], xr.Dataset)
+                else None
+            ),
             "init": False,
         }
         if dim is None:
@@ -1664,9 +1668,11 @@ def _compute_climatology(
         """
         input_dict = {
             "ensemble": self._datasets["initialized"],
-            "control": self._datasets["control"]
-            if isinstance(self._datasets["control"], xr.Dataset)
-            else None,
+            "control": (
+                self._datasets["control"]
+                if isinstance(self._datasets["control"], xr.Dataset)
+                else None
+            ),
             "init": True,
         }
         if dim is None:
@@ -2877,11 +2883,9 @@ def remove_bias(
             if len(self.get_initialized().coords["valid_time"].dims) == 1 and set(
                 ["init", "lead"]
             ).issubset(set(self.get_initialized().dims)):
-                self._datasets["initialized"].coords[
-                    "valid_time"
-                ] = add_time_from_init_lead(
-                    self.get_initialized().drop("valid_time")
-                ).coords[
-                    "valid_time"
-                ]
+                self._datasets["initialized"].coords["valid_time"] = (
+                    add_time_from_init_lead(
+                        self.get_initialized().drop("valid_time")
+                    ).coords["valid_time"]
+                )
         return self

climpred/graphics.py

@@ -415,9 +415,11 @@ def _verif_dates_xr(hindcast, alignment, reference, date2num_units):
         hindcast.get_observations(),
         alignment,
         reference=reference,
-        hist=hindcast.get_uninitialized()
-        if isinstance(hindcast.get_uninitialized(), xr.Dataset)
-        else None,
+        hist=(
+            hindcast.get_uninitialized()
+            if isinstance(hindcast.get_uninitialized(), xr.Dataset)
+            else None
+        ),
     )
 
     verif_dates_xr = xr.concat(

climpred/tests/test_bias_removal.py

@@ -1,4 +1,5 @@
 """Test bias_removal.py."""
+
 import copy
 
 import numpy as np

climpred/tests/test_graphics.py

@@ -1,4 +1,5 @@
 """Test graphics.py and PredictionEnsemble.plot()."""
+
 import pytest
 
 from climpred import HindcastEnsemble, PerfectModelEnsemble

climpred/tests/test_metrics_perfect.py

@@ -167,9 +167,11 @@ def f(x):
         skill = he.verify(
             metric=metric,
             comparison="m2o",
-            dim=["member", "init"]
-            if metric in probabilistic_metrics_requiring_more_than_member_dim
-            else "member",
+            dim=(
+                ["member", "init"]
+                if metric in probabilistic_metrics_requiring_more_than_member_dim
+                else "member"
+            ),
             alignment=alignment,
             **metric_kwargs,
         ).SST

climpred/tests/test_perfect_model_prediction.py

@@ -1,6 +1,5 @@
 """Test compute_perfect_model."""
 
-
 import pytest
 import xarray as xr
 

climpred/tests/test_relative_entropy.py

@@ -1,4 +1,5 @@
 """Test relative_entropy.py"""
+
 import pytest
 
 from climpred.graphics import plot_relative_entropy

climpred/tests/test_uninitialized.py

@@ -1,4 +1,5 @@
 """Test compute_uninitialized."""
+
 import pytest
 
 from climpred.constants import VALID_ALIGNMENTS

climpred/utils.py

@@ -86,9 +86,9 @@ def assign_attrs(
         skill[v].attrs.update(ds[v].attrs)
 
     # climpred info
-    skill.attrs[
-        "prediction_skill_software"
-    ] = "climpred https://climpred.readthedocs.io/"
+    skill.attrs["prediction_skill_software"] = (
+        "climpred https://climpred.readthedocs.io/"
+    )
     if function_name:
         skill.attrs["skill_calculated_by_function"] = function_name
     if "init" in ds.coords and "init" not in skill.dims:
@@ -138,9 +138,9 @@ def assign_attrs(
     if "pers_sig" in kwargs:
         if kwargs["pers_sig"] is not None:
             _, ci_low_pers, ci_high_pers = _p_ci_from_sig(kwargs["pers_sig"])
-            kwargs[
-                "confidence_interval_levels_persistence"
-            ] = f"{ci_high_pers}-{ci_low_pers}"
+            kwargs["confidence_interval_levels_persistence"] = (
+                f"{ci_high_pers}-{ci_low_pers}"
+            )
     # check for none attrs and remove
     del_list = []
     for key, value in kwargs.items():