xs reconstruction improve

sandy/core/xs.py

@@ -465,30 +465,20 @@ def _reconstruct_sums(self, drop=True, inplace=False):
         """
         df = self.data.copy()
         for mat in self.data.columns.get_level_values("MAT").unique():
-            for parent, daughters in sorted(redundant_xs.items(), reverse=True):
-                daughters = [x for x in daughters if x in df[mat]]
-                if daughters:
-                    df[mat,parent] = df[mat][daughters].sum(axis=1)
+            parent = pd.Index(self.__class__.redundant_xs.keys())
+            mask = parent.isin(df.columns.get_level_values('MT'))
+            for father in parent[mask]:
+                mask = df.columns.get_level_values('MT')\
+                         .isin(self.__class__.redundant_xs[father])
+                df[mat, father] = df.loc[:, mask].sum(axis=1)
             # keep only mts present in the original file
             if drop:
-                todrop = [x for x in df[mat].columns if x not in self.data[mat].columns]
-                cols_to_drop = pd.MultiIndex.from_product([[mat], todrop])
-                df.drop(cols_to_drop, axis=1, inplace=True)
+                columns = df.columns.intersection(self.data.columns)
+                df = df.loc[:, columns]
         if inplace:
             self.data = df
         else:
-            return Xs(df)
-#        frame = self.copy()
-#        for mat in frame.columns.get_level_values("MAT").unique():
-#            for parent, daughters in sorted(Xs.redundant_xs.items(), reverse=True):
-#                daughters = [ x for x in daughters if x in frame[mat]]
-#                if daughters:
-#                    frame[mat,parent] = frame[mat][daughters].sum(axis=1)
-#            # keep only mts present in the original file
-#            if drop:
-#                todrop = [ x for x in frame[mat].columns if x not in self.columns.get_level_values("MT") ]
-#                frame.drop(pd.MultiIndex.from_product([[mat], todrop]), axis=1, inplace=True)
-#        return Xs(frame)
+            return self.__class__(df)
 
     def _perturb(self, pert, method=2, **kwargs):
         """Perturb cross sections/nubar given a set of perturbations.