reformat to make linter happy

matminer/featurizers/composition/alloy.py

@@ -766,7 +766,7 @@ def compute_lambda(yang_delta, entropy):
             float
         """
         if yang_delta != 0:
-            return entropy / yang_delta ** 2
+            return entropy / yang_delta**2
         else:
             return 0
 
@@ -791,8 +791,8 @@ def compute_gamma_radii(miracle_radius_stats):
         mrmin = miracle_radius_stats["min"]
         mrmax = miracle_radius_stats["max"]
 
-        numerator = 1 - np.sqrt((mrmean * mrmin + mrmin ** 2) / (mrmean + mrmin) ** 2)
-        denominator = 1 - np.sqrt((mrmean * mrmax + mrmax ** 2) / (mrmean + mrmax) ** 2)
+        numerator = 1 - np.sqrt((mrmean * mrmin + mrmin**2) / (mrmean + mrmin) ** 2)
+        denominator = 1 - np.sqrt((mrmean * mrmax + mrmax**2) / (mrmean + mrmax) ** 2)
         return numerator / denominator
 
     @staticmethod

matminer/featurizers/site/fingerprint.py

@@ -262,7 +262,7 @@ def featurize(self, struct, idx):
                         site_list.append(n)
                         this_av_inv_drel += 1.0 / (neigh_dist[j][1])
                 this_av_inv_drel = this_av_inv_drel / float(this_cn)
-                d_fac = this_av_inv_drel ** self.dist_exp
+                d_fac = this_av_inv_drel**self.dist_exp
                 for cn in range(max(2, prev_cn + 1), min(this_cn + 1, 13)):
                     # Set all OPs of non-CN-complying neighbor environments
                     # to zero if applicable.

matminer/featurizers/site/rdf.py

@@ -74,7 +74,7 @@ def g2(eta, rs, cutoff):
         Returns:
             (float) Gaussian radial symmetry function.
         """
-        ridge = np.exp(-eta * (rs ** 2.0) / (cutoff ** 2.0)) * GaussianSymmFunc.cosine_cutoff(rs, cutoff)
+        ridge = np.exp(-eta * (rs**2.0) / (cutoff**2.0)) * GaussianSymmFunc.cosine_cutoff(rs, cutoff)
         return ridge.sum()
 
     @staticmethod
@@ -118,7 +118,7 @@ def g4(etas, zetas, gammas, neigh_dist, neigh_coords, cutoff):
             ind = 0
             for eta in etas:
                 # Compute the eta term
-                eta_term = np.exp(-eta * (r_ij ** 2.0 + r_ik ** 2.0 + r_jk ** 2.0) / (cutoff ** 2.0)) * cutoff_fun
+                eta_term = np.exp(-eta * (r_ij**2.0 + r_ik**2.0 + r_jk**2.0) / (cutoff**2.0)) * cutoff_fun
                 for zeta in zetas:
                     for gamma in gammas:
                         term = (1.0 + gamma * cos_theta) ** zeta * eta_term

matminer/featurizers/site/tests/test_fingerprint.py

@@ -68,9 +68,9 @@ def test_off_center_cscl(self):
         self.assertArrayAlmostEqual(-1 * site1[8:], site2[8:])
 
         # Make sure the site-ones are as expected.
-        right_dist = 4.209 * np.sqrt(0.45 ** 2 + 2 * 0.5 ** 2)
+        right_dist = 4.209 * np.sqrt(0.45**2 + 2 * 0.5**2)
         right_xdist = 4.209 * 0.45
-        left_dist = 4.209 * np.sqrt(0.55 ** 2 + 2 * 0.5 ** 2)
+        left_dist = 4.209 * np.sqrt(0.55**2 + 2 * 0.5**2)
         left_xdist = 4.209 * 0.55
         self.assertAlmostEqual(
             4

matminer/featurizers/structure/bonding.py

@@ -421,7 +421,7 @@ def _approximate_bonds(self, local_bonds):
                     d0 = u_mends[0] - l_mends[0]
                     d1 = u_mends[1] - l_mends[1]
 
-                    d = (d0 ** 2.0 + d1 ** 2.0) ** 0.5
+                    d = (d0**2.0 + d1**2.0) ** 0.5
                     if not d_min:
                         d_min = d
                         nearest = [abss]

matminer/featurizers/structure/order.py

@@ -62,7 +62,7 @@ def featurize(self, s):
                 raise ValueError("Disordered structure support not built yet.")
             total_rad = 0
             for site in s:
-                total_rad += site.specie.atomic_radius ** 3
+                total_rad += site.specie.atomic_radius**3
             output.append(4 * math.pi * total_rad / (3 * s.volume))
 
         return output

matminer/featurizers/utils/grdf.py

@@ -65,7 +65,7 @@ def volume(self, cutoff):
             (float): Volume of bin
         """
 
-        results = integrate.quad(lambda x: 4.0 * pi * self(x) * x ** 2.0, 0, cutoff)
+        results = integrate.quad(lambda x: 4.0 * pi * self(x) * x**2.0, 0, cutoff)
         if results[1] > 1e-5:
             raise ValueError("Numerical integration fails for this function." " Please implement analytic integral")
         return results[0]
@@ -92,7 +92,7 @@ def __call__(self, r_ij):
         )
 
     def volume(self, cutoff):
-        return 4.0 / 3 * np.pi * (min(self.start + self.width, cutoff) ** 3 - self.start ** 3)
+        return 4.0 / 3 * np.pi * (min(self.start + self.width, cutoff) ** 3 - self.start**3)
 
 
 class Gaussian(AbstractPairwise):
@@ -117,7 +117,7 @@ def volume(self, cutoff):
             * self.width
             * (
                 np.sqrt(pi)
-                * (2 * self.center ** 2 + self.width ** 2)
+                * (2 * self.center**2 + self.width**2)
                 * (erf((cutoff - self.center) / self.width) + erf(self.center / self.width))
                 + 2 * self.width * (self.center * self(0) - (self.center + cutoff) * self(cutoff))
             )
@@ -143,7 +143,7 @@ def volume(self, cutoff):
             4
             * pi
             * (((self.a * cutoff) ** 2 - 2) * np.sin(self.a * cutoff) + 2 * self.a * cutoff * np.cos(self.a * cutoff))
-            / self.a ** 3
+            / self.a**3
         )
 
 
@@ -170,7 +170,7 @@ def volume(self, cutoff):
                 + 2 * self.a * cutoff * np.sin(self.a * cutoff)
                 - 2
             )
-            / self.a ** 3
+            / self.a**3
         )
 
 

matminer/featurizers/utils/stats.py

@@ -173,7 +173,7 @@ def skewness(data_lst, weights=None):
             u2 = np.dot(weights, np.power(diff, 2)) / total_weight
             if np.isclose(u3, 0):
                 return 0
-            return u3 / u2 ** 1.5
+            return u3 / u2**1.5
 
     @staticmethod
     def kurtosis(data_lst, weights=None):
@@ -203,7 +203,7 @@ def kurtosis(data_lst, weights=None):
             u2 = np.dot(weights, diff_sq)
             if np.isclose(u4, 0):
                 return 0
-            return u4 / u2 ** 2 * total_weight
+            return u4 / u2**2 * total_weight
 
     @staticmethod
     def geom_std_dev(data_lst, weights=None):

matminer/featurizers/utils/tests/test_grdf.py

@@ -35,7 +35,7 @@ def test_gaussian(self):
     def test_histogram(self):
         h = Histogram(1, 4)
         self.assertArrayAlmostEqual([0, 1, 0], h([0.5, 2, 5]))
-        self.assertAlmostEqual(h.volume(10), 4 / 3.0 * np.pi * (5 ** 3 - 1 ** 3))
+        self.assertAlmostEqual(h.volume(10), 4 / 3.0 * np.pi * (5**3 - 1**3))
 
     def test_cosine(self):
         c = Cosine(2)

matminer/utils/kernels.py

@@ -18,4 +18,4 @@ def gaussian_kernel(arr0, arr1, SIGMA):
     kernel trick.
     """
     diff = arr0 - arr1
-    return np.exp(-np.linalg.norm(diff.A1, ord=2) ** 2 / 2 / SIGMA ** 2)
+    return np.exp(-np.linalg.norm(diff.A1, ord=2) ** 2 / 2 / SIGMA**2)