update pre-commit hooks, update to stable black

.pre-commit-config.yaml

@@ -2,7 +2,7 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v4.1.0
     hooks:
     -   id: trailing-whitespace
     -   id: end-of-file-fixer
@@ -13,15 +13,15 @@ repos:
     -   id: check-case-conflict
     -   id: check-docstring-first
 -   repo: https://github.com/pycqa/isort
-    rev: 5.8.0
+    rev: "5.10.1"
     hooks:
     -   id: isort
 -   repo: https://github.com/psf/black
-    rev: 21.6b0
+    rev: "22.1.0"
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/flake8
-    rev: 3.9.2
+    rev: "4.0.1"
     hooks:
     -   id: flake8
         additional_dependencies: [flake8-docstrings]

.zenodo.json

@@ -80,4 +80,4 @@
             "resource_type": "software"
         }
     ]
-  }
+  }

examples/run_full_prep.py

@@ -129,7 +129,7 @@
 EEG_raw_matlab = sio.loadmat(fname_mat1)
 EEG_raw_matlab = EEG_raw_matlab["save_data"]
 EEG_raw_diff = EEG_raw - EEG_raw_matlab
-EEG_raw_mse = (EEG_raw_diff / EEG_raw_max ** 2).mean(axis=None)
+EEG_raw_mse = (EEG_raw_diff / EEG_raw_max**2).mean(axis=None)
 
 fig, axs = plt.subplots(5, 3, sharex="all", figsize=(16, 12))
 plt.setp(fig, facecolor=[1, 1, 1])

pyprep/utils.py

@@ -283,7 +283,7 @@ def _eeglab_calc_g(pos_from, pos_to, stiffness=4, num_lterms=7):
     # Calculate Legendre coefficients for the given degree and stiffness
     factors = [0]
     for n in range(1, num_lterms + 1):
-        f = (2 * n + 1) / (n ** stiffness * (n + 1) ** stiffness * 4 * np.pi)
+        f = (2 * n + 1) / (n**stiffness * (n + 1) ** stiffness * 4 * np.pi)
         factors.append(f)
 
     return legval(EI, factors)
@@ -452,8 +452,8 @@ def _correlate_arrays(a, b, matlab_strict=False):
 
     """
     if matlab_strict:
-        SSa = np.sum(a ** 2, axis=1)
-        SSb = np.sum(b ** 2, axis=1)
+        SSa = np.sum(a**2, axis=1)
+        SSb = np.sum(b**2, axis=1)
         SSab = np.sum(a * b, axis=1)
         return SSab / (np.sqrt(SSa) * np.sqrt(SSb))
     else:

tests/test_removeTrend.py

@@ -27,9 +27,9 @@ def test_highpass():
     error1 = lowpass_filt1 - highfreq_signal
     error2 = lowpass_filt2 - highfreq_signal
     error3 = lowpass_filt3 - highfreq_signal
-    assert np.sqrt(np.mean(error1 ** 2)) < 0.1
-    assert np.sqrt(np.mean(error2 ** 2)) < 0.1
-    assert np.sqrt(np.mean(error3 ** 2)) < 0.1
+    assert np.sqrt(np.mean(error1**2)) < 0.1
+    assert np.sqrt(np.mean(error2**2)) < 0.1
+    assert np.sqrt(np.mean(error3**2)) < 0.1
 
 
 def test_detrend():
@@ -45,4 +45,4 @@ def test_detrend():
         signal_trend, detrendType="Local detrend", sample_rate=100
     )
     error3 = signal_detrend - signal
-    assert np.sqrt(np.mean(error3 ** 2)) < 0.1
+    assert np.sqrt(np.mean(error3**2)) < 0.1