black .

antropy/entropy.py

@@ -124,9 +124,7 @@ def perm_entropy(x, order=3, delay=1, normalize=False):
     """
     # If multiple delay are passed, return the average across all d
     if isinstance(delay, (list, np.ndarray, range)):
-        return np.mean(
-            [perm_entropy(x, order=order, delay=d, normalize=normalize) for d in delay]
-        )
+        return np.mean([perm_entropy(x, order=order, delay=d, normalize=normalize) for d in delay])
     x = np.array(x)
     ran_order = range(order)
     hashmult = np.power(order, ran_order)
@@ -442,9 +440,7 @@ def _numba_sampen(sequence, order, r):
         prev_in_diff = int(abs(sequence[order] - sequence[offset + order]) >= r)
         for idx in range(1, size - offset - order):
             out_diff = int(abs(sequence[idx - 1] - sequence[idx + offset - 1]) >= r)
-            in_diff = int(
-                abs(sequence[idx + order] - sequence[idx + offset + order]) >= r
-            )
+            in_diff = int(abs(sequence[idx + order] - sequence[idx + offset + order]) >= r)
             n_numerator += in_diff - out_diff
             n_denominator += prev_in_diff - out_diff
             prev_in_diff = in_diff

antropy/fractal.py

@@ -194,9 +194,9 @@ def katz_fd(x, axis=-1):
 
     # max distance from first to all
     horizontal_diffs = np.arange(1, x.shape[axis])
-    vertical_diffs = np.take(
-        x, indices=np.arange(1, x.shape[axis]), axis=axis
-    ) - np.take(x, indices=[0], axis=axis)
+    vertical_diffs = np.take(x, indices=np.arange(1, x.shape[axis]), axis=axis) - np.take(
+        x, indices=[0], axis=axis
+    )
 
     if axis == 1:  # reshape if needed
         horizontal_diffs = horizontal_diffs.reshape(1, -1)
@@ -209,9 +209,7 @@ def katz_fd(x, axis=-1):
 
     # Katz Fractal Dimension Calculation
     full_distance = np.log10(total_path_length / average_path_length)
-    kfd = np.squeeze(
-        full_distance / (full_distance + np.log10(max_distance / total_path_length))
-    )
+    kfd = np.squeeze(full_distance / (full_distance + np.log10(max_distance / total_path_length)))
 
     # ensure scalar output
     if not kfd.ndim:

antropy/tests/test_entropy.py

@@ -49,9 +49,7 @@ def test_spectral_entropy(self):
         spectral_entropy(RANDOM_TS, SF_TS, method="fft")
         spectral_entropy(RANDOM_TS, SF_TS, method="welch")
         spectral_entropy(RANDOM_TS, SF_TS, method="welch", nperseg=400)
-        self.assertEqual(
-            np.round(spectral_entropy(RANDOM_TS, SF_TS, normalize=True), 1), 0.9
-        )
+        self.assertEqual(np.round(spectral_entropy(RANDOM_TS, SF_TS, normalize=True), 1), 0.9)
         self.assertEqual(np.round(spectral_entropy(PURE_SINE, 100), 2), 0.0)
         # 2D data
         params = dict(sf=SF_TS, normalize=True, method="welch", nperseg=100)

antropy/utils.py

@@ -47,9 +47,7 @@ def _embed(x, order=3, delay=1):
         # pre-defiend an empty list to store numpy.array (concatenate with a list is faster)
         embed_signal_length = N - (order - 1) * delay
         # define the new signal length
-        indice = [
-            [(i * delay), (i * delay + embed_signal_length)] for i in range(order)
-        ]
+        indice = [[(i * delay), (i * delay + embed_signal_length)] for i in range(order)]
         # generate a list of slice indice on input signal
         for i in range(order):
             # loop with the order