[pre-commit.ci] pre-commit autoupdate (#229)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 22.12.0 → 23.3.0](psf/black@22.12.0...23.3.0)
- [github.com/PyCQA/isort: 5.11.4 → 5.12.0](PyCQA/isort@5.11.4...5.12.0)

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

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

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -2,12 +2,12 @@ ci:
   autoupdate_schedule: "quarterly"
 repos:
 - repo: https://github.com/psf/black
-  rev: 22.12.0
+  rev: 23.3.0
   hooks:
   - id: black
     files: ^physical_validation
 - repo: https://github.com/PyCQA/isort
-  rev: 5.11.4
+  rev: 5.12.0
   hooks:
   - id: isort
     files: ^physical_validation

physical_validation/data/unit_data.py

@@ -47,7 +47,6 @@ def __init__(
         pressure_str: str = "PRESS",
         time_str: str = "TIME",
     ):
-
         self.__kb = float(kb)
         self.__energy_str = str(energy_str)
         self.__energy_conversion = float(energy_conversion)

physical_validation/tests/test_data_observable_data.py

@@ -21,7 +21,6 @@
 
 
 def test_observable_data_getters_and_setters() -> None:
-
     # Check that newly created observable data object has `None` frames
     observable_data = ObservableData()
     assert observable_data.nframes is None

physical_validation/tests/test_data_trajectory_data.py

@@ -21,7 +21,6 @@
 
 
 def test_trajectory_data_getters_and_setters() -> None:
-
     num_frames = 3
     num_atoms = 5
     position = np.random.random((num_frames, num_atoms, 3))

physical_validation/util/ensemble.py

@@ -84,7 +84,6 @@ def chemical_potential_energy(
 def generate_histograms(
     traj1: np.ndarray, traj2: np.ndarray, g1: float, g2: float, bins: np.ndarray
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-
     n1 = np.size(traj1)
     n2 = np.size(traj2)
 
@@ -110,7 +109,6 @@ def do_linear_fit(
     xlabel: str,
     ylabel: str,
 ) -> Tuple[np.ndarray, np.ndarray]:
-
     h1, h2, dh1, dh2 = generate_histograms(traj1, traj2, g1, g2, bins)
 
     #  v  copied from checkensemble.py  v
@@ -187,7 +185,6 @@ def do_max_likelihood_fit(
     init_params: np.ndarray,
     verbose: bool,
 ) -> Tuple[np.ndarray, np.ndarray]:
-
     # ============================================================= #
     # Define (negative) log-likelihood function and its derivatives #
     # ============================================================= #
@@ -1136,7 +1133,6 @@ def check_1d(
             trajectory.bootstrap(traj2, bootstrap_repetitions),
         )
     ):
-
         # use overlap region
         t1, t2, min_ene, max_ene = trajectory.overlap(traj1=t1, traj2=t2)
         # calculate inefficiency

physical_validation/util/gromacs_interface.py

@@ -41,7 +41,6 @@ def __init__(
         dp: bool = False,
         includepath: Optional[Union[str, List[str]]] = None,
     ):
-
         self._exe = None
         self._dp = False
         self._includepath = None
@@ -110,7 +109,6 @@ def get_quantities(
         end: Optional[float] = None,
         args: Optional[List[str]] = None,
     ) -> Dict[str, np.ndarray]:
-
         if args is None:
             args = []
 

physical_validation/util/integrator.py

@@ -53,7 +53,6 @@ def check_convergence(
     screen: bool,
     filename: Optional[str],
 ) -> float:
-
     assert isinstance(const_traj, dict)
     assert len(const_traj) >= 2
 

physical_validation/util/plot.py

@@ -35,7 +35,6 @@ def plot(
     filename: Optional[str] = None,
     screen: bool = True,
 ) -> None:
-
     try:
         import matplotlib as mpl
         import matplotlib.pyplot as plt