[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
       - id: debug-statements
       - id: check-ast
   - repo: https://github.com/psf/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
       - id: black
   - repo: https://github.com/asottile/pyupgrade

adaptive/learner/learner1D.py

@@ -342,7 +342,8 @@ def vdim(self) -> int:
 
     def to_numpy(self):
         """Data as NumPy array of size ``(npoints, 2)`` if ``learner.function`` returns a scalar
-        and ``(npoints, 1+vdim)`` if ``learner.function`` returns a vector of length ``vdim``."""
+        and ``(npoints, 1+vdim)`` if ``learner.function`` returns a vector of length ``vdim``.
+        """
         return np.array([(x, *np.atleast_1d(y)) for x, y in sorted(self.data.items())])
 
     def to_dataframe(

adaptive/learner/learner2D.py

@@ -407,7 +407,8 @@ def xy_scale(self) -> np.ndarray:
 
     def to_numpy(self):
         """Data as NumPy array of size ``(npoints, 3)`` if ``learner.function`` returns a scalar
-        and ``(npoints, 2+vdim)`` if ``learner.function`` returns a vector of length ``vdim``."""
+        and ``(npoints, 2+vdim)`` if ``learner.function`` returns a vector of length ``vdim``.
+        """
         return np.array(
             [(x, y, *np.atleast_1d(z)) for (x, y), z in sorted(self.data.items())]
         )

adaptive/runner.py

@@ -190,7 +190,6 @@ def __init__(
         raise_if_retries_exceeded: bool = True,
         allow_running_forever: bool = False,
     ):
-
         self.executor = _ensure_executor(executor)
         self.goal = _goal(
             learner,
@@ -638,7 +637,6 @@ def __init__(
         retries: int = 0,
         raise_if_retries_exceeded: bool = True,
     ) -> None:
-
         if (
             executor is None
             and _default_executor is concurrent.ProcessPoolExecutor
@@ -977,6 +975,7 @@ def _get_ncores(
 
 # --- Useful runner goals
 
+
 # TODO: deprecate
 def stop_after(*, seconds=0, minutes=0, hours=0) -> Callable[[BaseLearner], bool]:
     """Stop a runner after a specified time.

adaptive/tests/test_learner1d.py

@@ -128,7 +128,6 @@ def f(x):
 
 
 def test_termination_on_discontinuities():
-
     learner = _run_on_discontinuity(0, (-1, 1))
     smallest_interval = min(abs(a - b) for a, b in learner.losses.keys())
     assert smallest_interval >= np.finfo(float).eps