matplotlib
diff --git a/‎lib/matplotlib/lines.py
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+3-7Lines changed: 3 additions & 7 deletions b/‎lib/matplotlib/lines.py
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+3-7Lines changed: 3 additions & 7 deletions
diff --git a/‎lib/matplotlib/mlab.py
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+2-3Lines changed: 2 additions & 3 deletions b/‎lib/matplotlib/mlab.py
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+2-3Lines changed: 2 additions & 3 deletions
diff --git a/‎lib/matplotlib/tests/test_collections.py
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+1-1Lines changed: 1 addition & 1 deletion b/‎lib/matplotlib/tests/test_collections.py
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+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎lib/matplotlib/widgets.py
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+5-7Lines changed: 5 additions & 7 deletions b/‎lib/matplotlib/widgets.py
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+5-7Lines changed: 5 additions & 7 deletions
@@ -163,15 +163,11 @@ def _slice_or_none(in_v, slc):
             delta = np.empty((len(disp_coords), 2))
             delta[0, :] = 0
             delta[1:, :] = disp_coords[1:, :] - disp_coords[:-1, :]
-            delta = np.sum(delta**2, axis=1)
-            delta = np.sqrt(delta)
-            delta = np.cumsum(delta)
+            delta = np.hypot(*delta.T).cumsum()
             # calc distance between markers along path based on the axes
             # bounding box diagonal being a distance of unity:
-            scale = ax_transform.transform(np.array([[0, 0], [1, 1]]))
-            scale = np.diff(scale, axis=0)
-            scale = np.sum(scale**2)
-            scale = np.sqrt(scale)
+            (x0, y0), (x1, y1) = ax_transform.transform([[0, 0], [1, 1]])
+            scale = np.hypot(x1 - x0, y1 - y0)
             marker_delta = np.arange(start * scale, delta[-1], step * scale)
             # find closest actual data point that is closest to
             # the theoretical distance along the path:
 
@@ -1484,9 +1484,8 @@ def __init__(self, dataset, bw_method=None):
 
         self.covariance = self.data_covariance * self.factor ** 2
         self.inv_cov = self.data_inv_cov / self.factor ** 2
-        self.norm_factor = np.sqrt(
-            np.linalg.det(
-                2 * np.pi * self.covariance)) * self.num_dp
+        self.norm_factor = (np.sqrt(np.linalg.det(2 * np.pi * self.covariance))
+                            * self.num_dp)
 
     def scotts_factor(self):
         return np.power(self.num_dp, -1. / (self.dim + 4))
 
@@ -691,7 +691,7 @@ def test_pathcollection_legend_elements():
     l3 = ax.legend(h, l, loc=4)
 
     h, l = sc.legend_elements(prop="sizes", num=4, fmt="{x:.2f}",
-                           func=lambda x: 2*x)
+                              func=lambda x: 2*x)
     actsizes = [line.get_markersize() for line in h]
     labeledsizes = np.sqrt(np.array(l).astype(float)/2)
     assert_array_almost_equal(actsizes, labeledsizes)
 
@@ -1967,15 +1967,13 @@ def set_animated(self, val):
 
     def closest(self, x, y):
         """Return index and pixel distance to closest index."""
-        pts = np.transpose((self.x, self.y))
+        pts = np.column_stack([self.x, self.y])
         # Transform data coordinates to pixel coordinates.
         pts = self.ax.transData.transform(pts)
-        diff = pts - ((x, y))
-        if diff.ndim == 2:
-            dist = np.sqrt(np.sum(diff ** 2, axis=1))
-            return np.argmin(dist), np.min(dist)
-        else:
-            return 0, np.sqrt(np.sum(diff ** 2))
+        diff = pts - [x, y]
+        dist = np.hypot(*diff.T)
+        min_index = np.argmin(dist)
+        return min_index, dist[min_index]
 
 
 class RectangleSelector(_SelectorWidget):