NFC: cleaning up comments

pyomo/repn/plugins/standard_form.py

@@ -361,11 +361,6 @@ def write(self, model):
                 offset, linear_index, linear_data, _, _ = (
                     template_visitor.expand_expression(obj, obj.template_expr())
                 )
-                # <<<<<<< HEAD
-                #             N = len(repn.linear)
-                #             obj_data.append(self._to_vector(repn.linear))
-                #             obj_offset.append(repn.constant)
-                # =======
                 N = len(linear_index)
                 obj_index.append(map(var_order.__getitem__, linear_index))
                 obj_data.append(linear_data)
@@ -374,8 +369,6 @@ def write(self, model):
                 repn = visitor.walk_expression(obj.expr)
                 N = len(repn.linear)
                 obj_index.append(map(var_order.__getitem__, repn.linear))
-                # ESJ TODO: This miiiight need a _to_vector call but I think it
-                # doesn't anymore?
                 obj_data.append(repn.linear.values())
                 obj_offset.append(repn.constant)
 
@@ -386,12 +379,8 @@ def write(self, model):
                     )
 
             obj_nnz += N
-            # >>>>>>> templatized-writer
             if set_sense is not None and set_sense != obj.sense:
-                # ESJ TODO: This is gonna need _to_vector I think
-                obj_data[-1] = -self._to_vector(
-                    obj_data[-1], N, float
-                )  # -np.fromiter(obj_data[-1], float, N)
+                obj_data[-1] = -self._to_vector(obj_data[-1], N, float)
                 obj_offset[-1] *= -1
             obj_index_ptr.append(obj_index_ptr[-1] + N)
             if with_debug_timing:
@@ -460,12 +449,6 @@ def write(self, model):
                 )
 
             if mixed_form:
-                # <<<<<<< HEAD
-                #                 N = len(repn.linear)
-                #                 _data = self._to_vector(repn.linear)
-                #                 _index = np.fromiter(map(var_order.__getitem__, repn.linear), float, N)
-                #                 if ub == lb:
-                # =======
                 if lb == ub:
                     con_nnz += N
                     # >>>>>>> templatized-writer
@@ -519,12 +502,6 @@ def write(self, model):
                 con_index.append(linear_index)
                 con_index_ptr.append(con_nnz)
             else:
-                # <<<<<<< HEAD
-                #                 N = len(repn.linear)
-                #                 _data = self._to_vector(repn.linear)
-                #                 _index = np.fromiter(map(var_order.__getitem__, repn.linear), float, N)
-                # =======
-                # >>>>>>> templatized-writer
                 if ub is not None:
                     if lb is not None:
                         linear_index = list(linear_index)
@@ -552,68 +529,34 @@ def write(self, model):
         nCol = len(columns)
         # Convert the compiled data to scipy sparse matrices
         if obj_data:
-            # <<<<<<< HEAD
-            #             obj_data = np.concatenate(obj_data)
-            #             obj_index = np.concatenate(obj_index)
-            #         c = self._csc_matrix_from_csr(
-            #             obj_data, obj_index, obj_index_ptr, len(obj_index_ptr) - 1, len(columns)
-            #         )
-            #         if rows:
-            #             con_data = np.concatenate(con_data)
-            #             con_index = np.concatenate(con_index)
-            #         A = self._csc_matrix_from_csr(
-            #             con_data, con_index, con_index_ptr, len(rows), len(columns)
-            #         )
-            # =======
-            # ESJ TODO: This is gonna need _to_vector...
             obj_data = self._to_vector(
                 itertools.chain.from_iterable(obj_data), obj_nnz, np.float64
             )
-            # obj_data = np.fromiter(
-            #     itertools.chain.from_iterable(obj_data), np.float64, obj_nnz
-            # )
-            # obj_data = list(itertools.chain(*obj_data))
             obj_index = np.fromiter(
                 itertools.chain.from_iterable(obj_index), np.int32, obj_nnz
             )
-            # obj_index = list(itertools.chain(*obj_index))
             obj_index_ptr = np.array(obj_index_ptr, dtype=np.int32)
         c = self._csr_matrix(
             obj_data, obj_index, obj_index_ptr, len(obj_index_ptr) - 1, nCol
         )
-        # c = scipy.sparse.csr_array(
-        #     (obj_data, obj_index, obj_index_ptr), [len(obj_index_ptr) - 1, nCol]
-        # )
         c = c.tocsc()
         c.sum_duplicates()
         c.eliminate_zeros()
         if rows:
             con_data = self._to_vector(
                 itertools.chain.from_iterable(con_data), con_nnz, np.float64
             )
-            # con_data = np.fromiter(
-            #     itertools.chain.from_iterable(con_data), np.float64, con_nnz
-            # )
-            # con_data = list(itertools.chain(*con_data))
-            # con_index = np.fromiter(
-            #     itertools.chain.from_iterable(con_index), np.int32, con_nnz
-            # )
             con_index = self._to_vector(
                 itertools.chain.from_iterable(con_index), con_nnz, np.int32
             )
-            # con_index = list(itertools.chain(*con_index))
             con_index_ptr = np.array(con_index_ptr, dtype=np.int32)
         A = self._csr_matrix(con_data, con_index, con_index_ptr, len(rows), nCol)
-        # A = scipy.sparse.csr_array(
-        #     (con_data, con_index, con_index_ptr), [len(rows), nCol]
-        # )
         A = A.tocsc()
         A.sum_duplicates()
         A.eliminate_zeros()
 
         if with_debug_timing:
             timer.toc('Formed matrices', level=logging.DEBUG)
-        # >>>>>>> templatized-writer
 
         # Some variables in the var_map may not actually appear in the
         # objective or constraints (e.g., added from col_order, or