Applied black formatter

src/mdevaluate/chill.py

@@ -23,9 +23,9 @@ def a_ij(atoms, N=4, l=3):
     qilm = np.average(qijlm, axis=1)
     qil = np.sum(qilm * np.conj(qilm), axis=-1) ** 0.5
     aij = (
-            np.sum(qilm[:, np.newaxis, :] * np.conj(qilm[indices]), axis=-1)
-            / qil[:, np.newaxis]
-            / qil[indices]
+        np.sum(qilm[:, np.newaxis, :] * np.conj(qilm[indices]), axis=-1)
+        / qil[:, np.newaxis]
+        / qil[indices]
     )
     return np.real(aij), indices
 

src/mdevaluate/coordinates.py

@@ -590,4 +590,4 @@ def cylindrical_coordinates(frame, origin=None):
     z = frame[:, 2]
     radius = (x**2 + y**2) ** 0.5
     phi = np.arctan2(y, x)
-    return np.array([radius, phi, z]).T
+    return np.array([radius, phi, z]).T

src/mdevaluate/distribution.py

@@ -114,11 +114,12 @@ def calc_gr(
             as large as possible, depending on the available memory.
         returnx (opt.): If True the x ordinate of the histogram is returned.
     """
+
     def gr_frame(
-            atoms_a: CoordinateFrame,
-            atoms_b: CoordinateFrame,
-            bins: ArrayLike,
-            remove_intra: bool = False,
+        atoms_a: CoordinateFrame,
+        atoms_b: CoordinateFrame,
+        bins: ArrayLike,
+        remove_intra: bool = False,
     ):
         box = atoms_b.box
         n = len(atoms_a) / np.prod(np.diag(box))
@@ -434,6 +435,7 @@ def hbonds(
     else:
         return pairs[is_bond]
 
+
 def calc_cluster_sizes(frame, r_max=0.35):
     frame_PBC, indices_PBC = pbc_points(
         frame, frame.box, thickness=r_max + 0.1, index=True

src/mdevaluate/system.py

@@ -6,6 +6,7 @@ from typing import Iterable
 import pandas as pd
 from tables import NoSuchNodeError
 
+
 @dataclass(kw_only=True)
 class MDSystem(abc.ABC):
     load_only_results: bool = False

src/mdevaluate/utils.py

@@ -35,17 +35,17 @@ def five_point_stencil(xdata, ydata):
     See: https://en.wikipedia.org/wiki/Five-point_stencil
     """
     return xdata[2:-2], (
-            (-ydata[4:] + 8 * ydata[3:-1] - 8 * ydata[1:-3] + ydata[:-4])
-            / (3 * (xdata[4:] - xdata[:-4]))
+        (-ydata[4:] + 8 * ydata[3:-1] - 8 * ydata[1:-3] + ydata[:-4])
+        / (3 * (xdata[4:] - xdata[:-4]))
     )
 
 
 def filon_fourier_transformation(
-        time,
-        correlation,
-        frequencies=None,
-        derivative="linear",
-        imag=True,
+    time,
+    correlation,
+    frequencies=None,
+    derivative="linear",
+    imag=True,
 ):
     """
     Fourier-transformation for slow varrying functions. The filon algorithmus is
@@ -89,25 +89,25 @@ def filon_fourier_transformation(
     else:
         raise NotImplementedError(
             'Invalid approximation method {}. Possible values are "linear", "stencil" '
-            'or a list of values.'
+            "or a list of values."
         )
     time = time.reshape(-1, 1)
 
     integral = (
-                       np.cos(frequencies * time[1:]) - np.cos(frequencies * time[:-1])
-               ) / frequencies ** 2
+        np.cos(frequencies * time[1:]) - np.cos(frequencies * time[:-1])
+    ) / frequencies**2
     fourier = (derivative * integral).sum(axis=0)
 
     if imag:
         integral = (
-                1j
-                * (np.sin(frequencies * time[1:]) - np.sin(frequencies * time[:-1]))
-                / frequencies ** 2
+            1j
+            * (np.sin(frequencies * time[1:]) - np.sin(frequencies * time[:-1]))
+            / frequencies**2
         )
         fourier = (
-                fourier
-                + (derivative * integral).sum(axis=0)
-                + 1j * correlation[0] / frequencies
+            fourier
+            + (derivative * integral).sum(axis=0)
+            + 1j * correlation[0] / frequencies
         )
 
     return (
@@ -498,5 +498,5 @@ def timing(function):
         time_needed = end_time - start_time
         print(f"Finished in {int(time_needed // 60)} min " f"{int(time_needed % 60)} s")
         return result
-    return wrap
 
+    return wrap