120 def gamma_method(self, **kwargs):
121 """Apply the gamma method to the content of the Corr."""
122 for item in self.content:
-123 if not(item is None):
+123 if not (item is None):
124 if self.N == 1:
125 item[0].gamma_method(**kwargs)
126 else:
@@ -2926,7 +2926,7 @@ region indentified for this correlator.
140
141 if vector_l is None:
142 vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
-143 elif(vector_r is None):
+143 elif (vector_r is None):
144 vector_r = vector_l
145 if isinstance(vector_l, list) and not isinstance(vector_r, list):
146 if len(vector_l) != self.T:
@@ -2987,7 +2987,7 @@ By default it will return the lowest source, which usually means unsmeared-unsme
179 """
180 if self.N == 1:
181 raise Exception("Trying to pick item from projected Corr")
-182 newcontent = [None if(item is None) else item[i, j] for item in self.content]
+182 newcontent = [None if (item is None) else item[i, j] for item in self.content]
183 return Corr(newcontent)