Reformat the repository with Black

benchmarks/california_housing.py

@@ -29,19 +29,11 @@ def __init__(self, regressors: List[RegressorMixin]) -> None:
         self._regressors = regressors
 
     def predict(self, X):
-        return np.mean([
-            regressor.predict(X)
-            for regressor in self._regressors
-        ])
+        return np.mean([regressor.predict(X) for regressor in self._regressors])
 
 
-@parfun(
-    split=per_argument(dataframe=df_by_row),
-    combine_with=lambda regressors: MeanRegressor(list(regressors)),
-)
-def train_regressor(
-    dataframe: pd.DataFrame, feature_names: List[str], target_name: str
-) -> RegressorMixin:
+@parfun(split=per_argument(dataframe=df_by_row), combine_with=lambda regressors: MeanRegressor(list(regressors)))
+def train_regressor(dataframe: pd.DataFrame, feature_names: List[str], target_name: str) -> RegressorMixin:
 
     regressor = DecisionTreeRegressor()
     regressor.fit(dataframe[feature_names], dataframe[[target_name]])
@@ -52,17 +44,8 @@ def train_regressor(
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("n_workers", action="store", type=int)
-    parser.add_argument(
-        "--backend",
-        type=str,
-        choices=BACKEND_REGISTRY.keys(),
-        default="local_multiprocessing",
-    )
-    parser.add_argument(
-        "--backend_args",
-        type=str,
-        default="{}",
-    )
+    parser.add_argument("--backend", type=str, choices=BACKEND_REGISTRY.keys(), default="local_multiprocessing")
+    parser.add_argument("--backend_args", type=str, default="{}")
 
     args = parser.parse_args()
 
@@ -79,10 +62,10 @@ def train_regressor(
     with set_parallel_backend_context("local_single_process"):
         regressor = train_regressor(dataframe, feature_names, target_name)
 
-        duration = timeit.timeit(
-            lambda: train_regressor(dataframe, feature_names, target_name),
-            number=N_MEASURES
-        ) / N_MEASURES
+        duration = (
+            timeit.timeit(lambda: train_regressor(dataframe, feature_names, target_name), number=N_MEASURES)
+            / N_MEASURES
+        )
 
         print("Duration sequential:", duration)
 
@@ -91,9 +74,9 @@ def train_regressor(
     with set_parallel_backend_context(args.backend, **backend_args):
         regressor = train_regressor(dataframe, feature_names, target_name)
 
-        duration = timeit.timeit(
-            lambda: train_regressor(dataframe, feature_names, target_name),
-            number=N_MEASURES
-        ) / N_MEASURES
+        duration = (
+            timeit.timeit(lambda: train_regressor(dataframe, feature_names, target_name), number=N_MEASURES)
+            / N_MEASURES
+        )
 
         print("Duration parallel:", duration)

docs/source/conf.py

@@ -35,12 +35,7 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.doctest",
-    "sphinx_substitution_extensions",
-    "sphinx.ext.napoleon",
-]
+extensions = ["sphinx.ext.autodoc", "sphinx.ext.doctest", "sphinx_substitution_extensions", "sphinx.ext.napoleon"]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]

parfun/about.py

@@ -1 +1 @@
-__version__ = "7.0.7"
+__version__ = "7.0.8"

parfun/backend/scaler.py

@@ -155,8 +155,7 @@ def __setstate__(self, state: dict) -> None:
         self._client_kwargs = state["client_kwargs"]
 
         self._client_pool = ScalerClientPool(
-            scheduler_address=self._scheduler_address,
-            client_kwargs=self._client_kwargs,
+            scheduler_address=self._scheduler_address, client_kwargs=self._client_kwargs
         )
 
     def session(self) -> ScalerSession:

tests/backend/mixins.py

@@ -117,9 +117,9 @@ def test_task_duration(self):
         futures: Deque[ProfiledFuture] = deque()
         total_task_duration = 0
 
-        with profile() as process_time,                     \
-             profile(time.perf_counter_ns) as eslaped_time, \
-             self.backend().session() as session:
+        with profile() as process_time, profile(
+            time.perf_counter_ns
+        ) as eslaped_time, self.backend().session() as session:
             i = 0
             current_concurrency = 0
             while i < ITERATIONS:
@@ -142,7 +142,7 @@ def test_task_duration(self):
         delta = measured_duration * TOLERANCE
 
         if abs(measured_duration - total_task_duration) < delta:
-            warnings.warn(f"Excpected execution duration of {total_task_duration} ns, measured {measured_duration} ns.")
+            warnings.warn(f"Expected execution duration of {total_task_duration} ns, measured {measured_duration} ns.")
 
     def test_supports_nested_tasks(self):
         """Validates that the backend supports nested tasks if it reports it."""

tests/backend/test_dask.py

@@ -2,6 +2,7 @@
 
 try:
     from parfun.backend.dask import DaskLocalClusterBackend, DaskRemoteClusterBackend
+
     dask_available = True
 except ImportError:
     dask_available = False

tests/backend/test_scaler.py

@@ -1,8 +1,10 @@
 import unittest
 
 from parfun.backend.mixins import BackendEngine
+
 try:
     from parfun.backend.scaler import ScalerLocalBackend, ScalerRemoteBackend
+
     scaler_installed = True
 except ImportError:
     scaler_installed = False

tests/test_decorators.py

@@ -14,7 +14,10 @@
 from parfun.combine.dataframe import df_concat
 from parfun.decorators import parfun
 from parfun.entry_point import (
-    BACKEND_REGISTRY, get_parallel_backend, set_parallel_backend, set_parallel_backend_context
+    BACKEND_REGISTRY,
+    get_parallel_backend,
+    set_parallel_backend,
+    set_parallel_backend_context,
 )
 from parfun.partition.api import multiple_arguments, per_argument
 from parfun.partition.collection import list_by_chunk