line length tweaks

Author: garrettwrong

src/aspire/operators/wemd.py

@@ -2,9 +2,12 @@
 Wavelet-based approximate Earthmover's distance (EMD) for n-dimensional signals.
 
 This code is based on the following paper:
-    Sameer Shirdhonkar and David W. Jacobs. "Approximate earth mover’s distance in linear time." 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
+    Sameer Shirdhonkar and David W. Jacobs.
+    "Approximate earth mover’s distance in linear time."
+    2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
 
-More details are available in their technical report: CAR-TR-1025 CS-TR-4908 UMIACS-TR-2008-06.
+More details are available in their technical report:
+    CAR-TR-1025 CS-TR-4908 UMIACS-TR-2008-06.
 """
 
 import numpy as np
@@ -14,8 +17,9 @@
 def wemd_embed(arr, wavelet, level):
     """
     This function computes an embedding of Numpy arrays such that
-    for non-negative arrays that sum to one, the L1 distance between the resulting embeddings
-    is strongly equivalent to the Earthmover distance of the arrays.
+    for non-negative arrays that sum to one, the L1 distance between the
+    resulting embeddings is strongly equivalent to the Earthmover distance
+    of the arrays.
 
     :param arr: Numpy array
     :param level: Decomposition level of the wavelets
@@ -43,7 +47,8 @@ def wemd_embed(arr, wavelet, level):
 
 def wemd_norm(arr, wavelet, level):
     """
-    Wavelet-based norm used to approximate the Earthmover's distance between mass distributions specified as Numpy arrays (typically images or volumes).
+    Wavelet-based norm used to approximate the Earthmover's distance between
+    mass distributions specified as Numpy arrays (typically images or volumes).
 
     :param arr: Numpy array of the difference between the two mass distributions.
     :param level: Decomposition level of the wavelets
@@ -53,5 +58,6 @@ def wemd_norm(arr, wavelet, level):
     See https://pywavelets.readthedocs.io/en/latest/ref/wavelets.html#built-in-wavelets-wavelist
     :return: Approximated Earthmover's Distance
     """
+
     coefs = wemd_embed(arr, wavelet, level)
     return np.linalg.norm(coefs, ord=1)

tests/test_wemd.py

@@ -23,21 +23,26 @@ def _is_monotone(seq):
 
 
 class WEMDTestCase(TestCase):
-    """Test that the WEMD distance between smoothed disks of various radii, angles and distances is monotone in the Euclidean distance of their centers.
-    Note that this monotonicity isn't strictly required by the theory, but holds empirically."""
+    """
+    Test that the WEMD distance between smoothed disks of various radii,
+    angles and distances is monotone in the Euclidean distance of their centers.
+    Note that this monotonicity isn't strictly required by the theory,
+    but holds empirically.
+    """
 
     def test_wemd_norm(self):
         WIDTH = 64
         HEIGHT = 64
         CENTER_X = WIDTH // 2
         CENTER_Y = HEIGHT // 2
-        LEVEL = (
-            int(ceil(log2(max(WIDTH, HEIGHT)))) + 1
-        )  # A rule of thumb for the wavelet level is to take the log2 of the side length.
+        # A rule of thumb for the wavelet level is to take
+        # the log2 of the side length.
+        LEVEL = int(ceil(log2(max(WIDTH, HEIGHT)))) + 1
         WAVELET = "coif3"
         warnings.filterwarnings(
             "ignore",
-            message="Level value of .* is too high: all coefficients will experience boundary effects.",
+            message="Level value of .* is too high:"
+            " all coefficients will experience boundary effects.",
         )
         for radius in [1, 2, 3, 4, 5, 6, 7]:  # Just some random radii
             for angle in [